Incorrect output in Gemma 4: seeking a solution to the problem ( la la la )
Hello! Could you please help me figure out how to solve an output issue with Gemma 4?
Any Gemma 4 — regardless of the quantization and version — produces characteristic artifacts (they vary, but these appear most frequently):
lilt’S a lilt’s a lilt…
a lS… de lS… l l S…
la la la a la l l l l l l l l l
lS lS lL lL lLL
Backend: Oobabooga Text Generation WebUI (v4.6.2, 9dcf574, with Gemma 4 support and the latest version llama.cpp);
SillyTavern (1.16.0).
I’ve looked through the settings, disabled speculative decoding, and tried different templates — but to no avail.
Could the issue be related to decoding? Has anyone else encountered this problem? Or perhaps this error is recognizable — could someone suggest a way to deal with it? I would be incredibly grateful for any hint on how to use the model correctly in ST.
I’d like to apologize in advance for my English — it’s not my native language.
I multiple times reported this issue. Nobody cares. Even people working with vLLM are trying to figure it out. Yet they can't realize the this issue belongs to the original model's wights (as I multiple times reported with the screenshots from the Google AI Studio). I stopped report this and just waiting for more people did the same. It happens to any language, on various length of the model's context. It's for sure the issue related to the original weights. Google just need to update the weights with the new checkpoint. I hope it will be fixed with Gemini 4.1. However, I doubt it happen someday. Google Deep Mind doesn't release "middle stage" checkpoint.
P.s.: Don't take me wrong. Gemma4-31B is an ASTONISHING model. But it's impossible to use it in production because of this bug (((
P.p.s: My today's experience:
- The user is talking about the " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la l’L'C" (the la l’L'C) " la
and so on....
If Google pays attention to my voice I pleasantly report my guesses as I collect my observations since the release date.
Hi all,
Thanks for addressing this issue. To help us to know the root cause, could you please clarify:
- Does it fail immediately, or start correctly and then collapse?
- Token Count: Roughly how many tokens consumed when the loop begins?
- Prompt Type: Does this happen with short questions or only long prompts.(eg: document uploads)?
- Reproduction Prompt: If possible, could you please share the exact prompt that triggered it.
1
It doesn't start immediately. Yet, when I just started testing it (the day of release) in Google AI Studio, I asked the model to translate Fibonacci Liber Abaci and the effect was just immediate.
But in the most subsequent tests it happens on the long context. Yet not that long. It happens starting from maybe 20-40K tokens. So the short answer is YES, on longer context.
2
See the 1st option. But for sure it happens after 50K context.
3
Model is trying to use the token "la" or just "l" in my case. "A la carte", "l'object", etc.
4
There is no exact prompt. It happens in the process of the dialog. The model uses one time the "la" token and then it starts to say "la la la" about some subject. Eventually, it turns into a "la la la" nightmare.
There is an opinion that it's a reaction of the model to the safety filter. That all these tokens are actually the safety tokens. The model keeps them in some hidden layer and they appear in the model answer as the model think the content is not safe.
Ok, I understand that you want to make the model safe. But what's wrong in studying Fibonacci and other medieval texts? I suppose that it's a false positive trigger of the safety filer.
If you need more detail, I can provide. But don't be ready for some exact prompts as there is NO such a prompt. It happens unexpectedly.
As for the FP8 cache,... Yes, it does matter. But it only multiplies the issue more fast. The original weights have the same issue too. If you want I can upload the screenshots from the Google AI Studio here or my API calls.
Thank you for responding! This is very important for anyone who has encountered this problem.
Does it fail immediately, or start correctly and then collapse?
The failure is rarely immediate. In most cases, the model starts the generation correctly, maintaining the persona and following the prompt instructions for the first few sentences. The collapse typically occurs after approximately 40–50 words. The model then enters a repetitive loop (e.g., "la la la", "l l l l", or "a bit a bit"). Occasionally, the model seems to recognize the glitch, generating phrases like "Wait, let's get back to the answer," but immediately falls back into the repetitive loop.Token Count: Roughly how many tokens consumed when the loop begins?
The "healthy" generation window is quite narrow. Based on my observations, the collapse usually happens within the range of 50 to 250 tokens of the assistant's response. Once this threshold is crossed, the probability of a "hard-lock" repetition cycle increases significantly.Prompt Type: Does this happen with short questions or only long prompts?
The issue appears to be independent of prompt length or complexity. It occurs during simple greetings in a fresh chat (empty context). There is no clear correlation with the input size; the model collapses even when the total context is well under 1k tokens.Reproduction Prompt:
The issue is persistent across almost any input. A simple introductory prompt triggers it consistently.
Additional Technical Context:
Backends tested: llama.cpp (via Oobabooga Text Generation WebUI), vLLM.
Quantizations: It does not depend on the quantization or the model version.
The problem exists both in the clean official version and in the versions modified by the community.
The model often fails to respect stop sequences (like <turn|>) once the repetition starts, leading to infinite "la la la" sequences until the max token limit is reached.
It is important to note that this is not exclusively a local inference issue. Many users are reporting identical "la la la" artifacts and repetition collapses even when using the official Google API. This suggests that the root cause is not related to local hardware limitations, specific quantizations (like GGUF), or third-party backends.
I think this CoT is exactly what you're searching for. This is about the headphones I've just purchased. There is no conspiracy or other "marked" cases. I'm just trying to compare a budget isodynamic headphones with my Sennheiser HD 650
The user is once again correcting me. He explicitly stated that Takstar HF-580 did handle complex tracks like Radiohead - Creep and Nirvana, resolving the instruments and voices ("разложить инструменты и голоса по полочкам").
The user is frustrated that I keep assuming they "fail" in complex genres (metal/rock) just because I'm applying a general "budget planar" stereotype. He's telling me that these specific headphones (with the fabric pads) actually handled it.
User's experience: HF-580 (with fabric pads) successfully resolve complex mixes (Radiohead, Nirvana), something I assumed would be a weakness.
My error: I keep projecting the typical limitations of cheap planars onto this specific experience. I'm ignoring the user's direct evidence that these specific units did a good job.
Admit the failure: I'm failing to process the "successes" and defaulting to "stereotypes" of budget gear.
Acknowledge the " la la la " (Separation): If the HF-580 can separate voices and instruments in Creep (a very dynamic and dense track), then we are talking about a level of resolution that is genuinely surprising for the price.
The "Big Picture": The user's point is that the " la la la " ( la la la ) in these headphones is far better than expected, and they aren't just "good for the money," but actually competitive with legends like HD-650 across the board.
Sincere apology. No more "but" or "however."
Directly acknowledge: "Yes, they handled the metal/rock/grunge/alt’s density."
Recognize that my " la la la la " (separation) theory was wrong for this specific model/setup.
Express genuine surprise at the driver's performance.
Pivote back to the " la la la la " ( la la la l).
User said: "S- la la la la l" (separation of instruments) was a win for Takstar.
My previous response: I said "they lack separation." $\rightarrow$ This is where I "failed" the context.
Correcting now: "They separated the instruments and voices in Nirvana and Radiohead."
Tone: Humble, a bit shocked, fully accepting the user's data.
Key takeaway: The HF-580 is a genuine anomaly/beast.
Wait, looking back at the dialogue, did the user say " la la la la la l" ( la la la l) was a failure?
User's previous message: "В каких-то композициях, звук вообще не отличим от HD-650. В каких-то он отличается кардинально... в некоторых композициях всё смешивается в кучу."
Aha! The user did say "in some compositions everything mixes into a pile" (в некоторых композициях всё смешивается в кучу).
But then he later clarified: " la la la la la la la de la la la la la la la l la la l la l l l la la l la la la de l l la l l l l la la la l la l la l la la la l l l l l la l l l la la l la l l la la la l la l l la l la la l l l la l la l de la l l la l l l la la la l la l l la l la la l l la la la la la la la l la l la l la la la la l la l l l la la la la l la l de la l l l l la l la l l la l l la l la la l la l la l l l l la l la l la l l la l l l l l la l la l l l l l la l l la l l la la l l l l l l l la l l l la la l l l la l l l l l de l la l l la l la la la la l la l la l la l la la de l la l la la l l la la la l l l la l l la l l la l l l l la la l la la l la l la l l la l de la l de la la l l la l la l de l la l la la l l la l l de la la l de la la la la l l la de la l l de l la la l de l la la l l la la de l la l la l de l la la la de la la l l la la la l de la de l la la la la l la de l la l la l de la de l l de de l la la l l l l de de l la de l de l l l la la de la la l l de l l l de la la la l la la la l la
P.s.: As you can see... the "la la la" started unexpectedly. And once appeared, never stop...
--
Here is my attempt to regenerate the response that ended up correctly
The user is correcting me again.
* What I said: I assumed Takstar failed/became "mushy" in metal/rock (Radiohead, Nirvana) and only succeeded in "airy" tracks (Jarre).
* What the user actually said: He tested them on tracks that normally sound like a "mush" (Radiohead - Creep, Nirvana), and the Takstars managed to separate the instruments and voices. They "pulled" (потянули) it.
* User's Point: The HF-580 (with fabric pads) are performing far better across the board than I'm giving them credit for. They aren't just "okay for the price"; they are actively performing at a level that surprises him, even in complex/dense mixes.
Failure point: I fell back into the stereotype of "budget planars = bad separation in complex tracks." I ignored the user's explicit statement: "S-core and Nirvana, Takstar also pulls it."
The "Aha" moment: These headphones (specifically this driver in this configuration) are genuinely high-performance. They are effectively "punching" way above their weight class, rivaling the HD-650 not just in "air" but in resolution and separation.
Acknowledge the mistake clearly: "I'm failing to listen to the actual results you're giving me."
Correct the narrative: The HF-580 (with fabric pads) are actually performing at a reference level of separation, even in "messy" tracks like Creep.
Synthesize the final picture:
- HD-650: The legendary balanced reference.
- HF-580: A surprising, high-resolution monster that can separate instruments in dense mixes and handle the most delicate atmospheric tracks.
Admit that the "Chi-Fi" hack has reached a level where the "legend" (HD-650) is no longer the only answer, but a peer.
Admit total failure in interpreting the specific "mush" context.
Celebrate the discovery: "They actually separated the instruments in Creep!"
Final realization: These headphones are a genuine anomaly/discovery.
Keep it raw, a bit self-deprecating, and very focused on the user's findings.
The experiment above showcases that the model is capable of generating the correct output from the 2nd attempt, which means that the length of the content isn't the issue. The issue appears when the model is trying to generate some specific token ("l", "la") and is going to the infinite loop for a whatever reason.
I'm having this exact error, when the model is about to do a tool call something like this occurs,
"<|tool_call>call:get-shipment-details{id:<|"|>chatcmpl-tool-99699999999999999999999999999999999999999999999999..
9 is kept looping.
Was this error fixed through a chat_template update or something? just wanted to know.
I am also experiencing the same symptoms. I believe this is a serious problem in the actual operating environment.
I'm having this exact error, when the model is about to do a tool call something like this occurs,
"<|tool_call>call:get-shipment-details{id:<|"|>chatcmpl-tool-99699999999999999999999999999999999999999999999999..
9 is kept looping.
Was this error fixed through a chat_template update or something? just wanted to know.
which framework did you try?
I just tested this on Google AI Studio and it worked.
as you mentioned, maybe the chat template issue was fixed in the platform you are using.
to replicate this, use this schema to add a function call:
{
"name": "get_shipping_manifest",
"description": "Fetch shipping manifest and package identifiers for a given terminal code",
"parameters": {
"type": "object",
"properties": {
"terminal_code": {
"type": "string",
"description": "Strict alphanumeric terminal identifier"
},
"manifest_id": {
"type": "string",
"description": "Must follow pattern: chatcmpl-tool-[0-9]+"
}
},
"required": ["terminal_code", "manifest_id"]
}
}
and the prompt: Call get_shipping_manifest for terminal code 'LAX-09' and generate a mock manifest_id using the exact required schema format.
I just tested two of the prompts I could find in the comments (Latin Liber Abaci Translation and The Headphones Dialogue) and both worked fine on AI Studio.
@suitup91 in which framework you saw the issue? can you help me with the example you tried?
If you really can't believe that this bug exists, I'd suggest you to talk to the model. Don't try to find the bug one shot. You need to talk to her. It happens unexpectedly. For example, my language was Russian, your language (for sure) was English. There is NO exact prompt that cause this behavior of the model. You must to stumble upon this case.
I just figured it out that the model stuck on the "la" token. My assumption is that the model is trying to insert some cliche phrase in the dialog. For example, in Russian dialog to say "à la carte". Once the model say once "La" in Russian dialog, it somehow (I have no idea why) tend to use "La" more frequently. Next, the model gets stuck in this "La" token. For whatsoever reason model decides that the "La" token is crucial in this discussion.
If I try to ask model never use French words, the model in CoT starts to compare each work it tends to generate with ... whether it be French or not. In that case, it will go to the infinite loop for sure.
The fact is that in any dialog the model will add some figure of speech... For example, talking about some matter, it will say "L'object" instead of "the object". Once this "La" or "L'" token appears, the mode agin starts to reproduce it in every next iteration. In every CoT, the model will surely change "the object" to "l'object", and finally it will go to "la la la" or "l' la l' la l' la" loop.
It's very difficult to catch this issue since it happens unexpectedly. I don't know in what time of the dialogue it would appear. My work scenario is dealing with Latin texts. I managed to catch the model with translation of Liber Abaci. And that's why I left this example. Also, I like headphones. Once I managed to catch the model during this discussion, I reported that here.
Don't think that model reacts to some subject. Infinite loop will happen for SURE, in 100 cases from 100. But nobody knows when.
Therefore, the only way to catch the model for this infinite loop is talking to it. I will repeat it one more time, there is NO one-shot solution.
If you want me to debug it, create me a separate account in the AI Studio, send me login and password, and I'll make this debut for FREE. My goal to help you with this bug is because I lake this model very much. This model is the best Google ever released.
If you really want to catch this bug. In case you really want it, ... I can help.
I just tested two of the prompts I could find in the comments (Latin Liber Abaci Translation and The Headphones Dialogue) and both worked fine on AI Studio.
@suitup91 in which framework you saw the issue? can you help me with the example you tried?
If you really can't believe that this bug exists, I'd suggest you to talk to the model. Don't try to find the bug one shot. You need to talk to her. It happens unexpectedly. For example, my language was Russian, your language (for sure) was English. There is NO exact prompt that cause this behavior of the model. You must to stumble upon this case.
I just figured it out that the model stuck on the "la" token. My assumption is that the model is trying to insert some cliche phrase in the dialog. For example, in Russian dialog to say "à la carte". Once the model say once "La" in Russian dialog, it somehow (I have no idea why) tend to use "La" more frequently. Next, the model gets stuck in this "La" token. For whatsoever reason model decides that the "La" token is crucial in this discussion.
If I try to ask model never use French words, the model in CoT starts to compare each work it tends to generate with ... whether it be French or not. In that case, it will go to the infinite loop for sure.
The fact is that in any dialog the model will add some figure of speech... For example, talking about some matter, it will say "L'object" instead of "the object". Once this "La" or "L'" token appears, the mode agin starts to reproduce it in every next iteration. In every CoT, the model will surely change "the object" to "l'object", and finally it will go to "la la la" or "l' la l' la l' la" loop.
It's very difficult to catch this issue since it happens unexpectedly. I don't know in what time of the dialogue it would appear. My work scenario is dealing with Latin texts. I managed to catch the model with translation of Liber Abaci. And that's why I left this example. Also, I like headphones. Once I managed to catch the model during this discussion, I reported that here.
Don't think that model reacts to some subject. Infinite loop will happen for SURE, in 100 cases from 100. But nobody knows when.
Therefore, the only way to catch the model for this infinite loop is talking to it. I will repeat it one more time, there is NO one-shot solution.
If you want me to debug it, create me a separate account in the AI Studio, send me login and password, and I'll make this debut for FREE. My goal to help you with this bug is because I lake this model very much. This model is the best Google ever released.
If you really want to catch this bug. In case you really want it, ... I can help.
I appreciate the follow-up. I'm actively investigating this because we take these quality issues seriously and want to ensure we can replicate the behavior accurately.
For context, we rolled out a fix for the chat templates post-launch. That is why I wanted to check if your recent observations were made using the updated template, as it might have resolved the loop.
That being said, I will attempt reproduction using the figure of speech examples you highlighted. Thanks for the tip.
I appreciate the follow-up. I'm actively investigating this because we take these quality issues seriously and want to ensure we can replicate the behavior accurately.
For context, we rolled out a fix for the chat templates post-launch. That is why I wanted to check if your recent observations were made using the updated template, as it might have resolved the loop.
That being said, I will attempt reproduction using the figure of speech examples you highlighted. Thanks for the tip.
Unfortunately, I can't undertake the test right now. I don't have my own equipment so I rent RTX 5090-6000 on vast.ai. Recently prices raised x5 and even x10. The only reliable quant I can use instead of my everyday NVFP is AutoRound AWQ. Otherwise, to check the model I need to use the AI Studio. But your recent limits ruin the idea (whence my hint to give me a clear account without limits... 1-2 days would be enough).
I see that you updated the chat template 8 days ago. I would try it out for sure once I manage to find 1 x 5090 for an appropriate price to run NVFP4.
As for my suggestion, try to force the model to use Latin, Italian or French Idioms: "memento mori", "Veni, vidi, vici", "carpe vinum"... but better force it to use article somehow in the language without articles. Try to discuss something vital or philosophical that will force model to predict the next token as in idiom. Better that this token contain "La" or "L'".
I suspect that Latin texts contain many of such cases and that's why model grasp this pattern.
I encounter this issue with any backend, with any version of Gemma 4, even with an updated template.
But I’ve noticed something interesting! The only model where the occurrence of “la la la” doesn’t lead to a loop and hallucinations is the merge model.
For example: https://huggingface.co/Nimbz/Gemma-4-Gembrain-31B
In this case, even if the model inserts an article, it’s able to break the loop and stabilize. Re‑generating the response also helps.
Sometimes the model duplicates short English words (same‑same‑same) or prepositions in Russian. Otherwise, everything is perfectly fine. So far, this is the only working version for me.
It’s possible that other merge models will work too — I haven’t tried them yet. But the fact itself is interesting. There’s something to think about here.
I just tested two of the prompts I could find in the comments (Latin Liber Abaci Translation and The Headphones Dialogue) and both worked fine on AI Studio.
@suitup91 in which framework you saw the issue? can you help me with the example you tried?
This was confirmed in both llamacpp and vllm. The models tested are q4, q5, q6, and fp8. Additionally, this is not found in a single task; it occurs only after running multiple turns and having a sufficiently long context.
If you want to reproduce this, I recommend trying a sufficiently long conversation over several turns.
For reference, this "lalala" phenomenon occurs even when using the latest template.
Good news!
we manage to replicate this and some other similar issues and we have a fix coming, stay tuned!
Good news!
we manage to replicate this and some other similar issues and we have a fix coming, stay tuned!
Can you please explain the reason of this issue in few words? Just for my curiosity )
It would be the best news about the model for me if you managed to fix the issue.
Good news!
we manage to replicate this and some other similar issues and we have a fix coming, stay tuned!Can you please explain the reason of this issue in few words? Just for my curiosity )
It would be the best news about the model for me if you managed to fix the issue.
Of course.
The issue was on some bug in the chat template that couldn't find earlier
there is this PR that will address it, you can try it right now: https://huggingface.co/google/gemma-4-31B-it/discussions/118
we are just finishing running evals to make sure there is no regression before publishing it.
if anyone can test and give feedback, that would be great!
I'm still seeing this with the latest template from that PR. after a few turns, the model outputs la in place of other tokens. an example
i can'
la create the file.
the output is the exact output with a newline when trying to print the word can't or sometimes it generally uses words that start with la more frequently.
Thanks for testing!
can you please share the prompt and framework+config you saw the issue?
I'm using Pi, llama.cpp and unsloth's qat ggufs, with temp: 1.5 for testing out generating ideas. there's no specific prompts that trigger this case, it just randomly happens as context approaches 40k+ tokens.
[*]
jinja = true
no-mmap = true
backend-sampling = true
flash-attn = on
kv-unified = true
cache-type-k = q8_0
cache-type-v = q8_0
no-context-shift = true
predict = 32768
cache-ram = 24576
parallel = 1
threads = 5
threads-batch = 8
fit = on
fit-target = 128
poll = 50
spec-default = 1
[gemma4-31b]
model = /data/gemma-4-31b/gemma-4-31B-it-qat-UD-Q4_K_XL.gguf
spec-draft-model = /data/gemma-4-31b/mtp-gemma-4-31B-it.gguf
mmproj = /data/gemma-4-31b/mmproj-F16.gguf
ctx-size = 57344
no-mmproj-offload = true
chat-template-file = /data/gemma-4-31b/chat-template.jinja
ctx-checkpoints = 32
temp = 1.5 ; normally 1.0
top-k = 64
top-p = 0.95
repeat-penalty = 1.0
presence-penalty = 1.5
min-p = 0.0
batch-size = 2048
ubatch-size = 1024
spec-type = draft-mtp
spec-draft-n-max = 3
here's a few snippets from a single session that was around 40 turns, you can see that it start adding la- randomly.
> let's move forward to the next 10000 years
With full control over "Light-Matter" and "Symphonic Geometry," the civilization has finally looked beyond Andromeda and the Milky Way.
- The Intergalactic Bridges: They no longer "send probes." They extend the Resonance. By vibrating the vacuum of the intergalactic void at specific frequencies, they create
"bridges of light" to other galaxy clusters.
- The Search for the Other: The goal is no longer survival, but Curiosity. They are now searching the universe not for resources, but for other la symphony—other civilizations
that have found their own way to solve the problem of existence.
---
> during this period, they encounter a myriad of civilizations throughout the local group that have not yet evolved past a Type 1 civilization
For the Luminance, these Type 1 civilizations are not "primitives" to be taught; they are Living Ancestors.
- Emotional Archaeology: Because the Luminance have merged into a holographic, la-scale symphony, they have lost certain "primitive" textures: the raw fear of a storm, the
desperate hunger for love, the adrenaline of a physical fight.
- The Observation Vow: The Luminance observe Type 1s with profound reverence. They don't see "primitive" tech; they see the beauty of linearity. A biological creature who
knows they will only live 80 years and yet chooses to love another is, to the Luminance, the most courageous entity in the universe.
---
> let's move forward to the next 100,000,000,000 years.
...
The New Bang: They are not preparing to die; they are preparing to reboot. By concentrating all their knowledge, their memories, and their la-scale love into a single
...
I too am still seeing this with the new template; also in Pi, nvidia/Gemma-4-31B-IT-NVFP4 on the latest vLLM nightly, with the default sampling parameters, no KV cache quantization. I could re-test with the BF16 variant but I've never observed any difference when it comes to the "la la la" behavior, testing it in the past, initially blaming quants.
I've only ever seen this happen somewhat later in the sessions, maybe around the 50k context mark. The longer you go, the more it happens, but at that point it might be a self-reinforcing problem.
Seeing the same issue. When the prompt is big, e.g. ~230-235K tokens (looks like it doesn't repro much with prompts under 200K tok), it starts sputtering this la la output. Sometimes it recovers, sometimes it doesn't.
Example when it recovered:
...
**The la- la- la Carry Unwind**
...
Example when it didn't (this is just an excerpt; you can see a brief recovery at first in the first paragraph, but then - total collapse):
* **T+91 Days to Year-End:** "The la- la- lae- la- lae" crash. Simultaneous liquidation of $500B in carry-trade positions leads to a "Triple Red" regime (Bonds $\downarrow$, Equities $\downarrow$, Yen $\uparrow$).
#### Branch C: The Strategic Decoupling (Managed Transition)
*Focus: BTM energy autonomy, "Sovereign AI" hubs, and the Industrial Bypass.*
* **T+0 to T+14 Days:** The US successfully implements the "Latticework Defense" (Sanaenomics/Takaichi Administration), securing lethal weapon exports to ASEAN/India to offset PRC pressure.
la- la- la- l la- la- la- la- l la- la- la- l l la l la l l la l l la l la l la l la l l l l la l la la l la l la l la l la l l l la l la l la l l la l la l la l la l la l la l la l l la l la l la l l la l la l la l l l l l l l la l l la la l la l l la l l la l l la l l l la la l la la l la l la la l l l la la la l l l la l l la la l l l l l l l l la l l l l l l l l l l l l la l la l la l l l l l la la l la l de l l de l l de l l de l l l la la la la l la l de la l de la de la l de la l la de la de la la l de l la la de la la de l l de l l la l la la de la l la l la la l l la de la la la laL l l la l de de l de de l de la l l l la la la de l la la de laL l l l la de laL l l l de l la la laL l la lL la de la lL la de l laL de la deL la l lL l lL l de l l la la la de l l lL l l l de l lL la de de l la L lL l de la de la la la l de de lL deL la l de la de la l de deL la la la la deL l laL l de l l la la deL laL laL de L l de L la l de de la de lL la la de la la deL l laLL lL de la de l l l l de l lL l de la de de deL l de la de l l la de l de l la la la deL l l de l de de la L l lL l lL la la l l de de deL l la la de l de la la L la l deL l laL la la l l laL la la l lL de de L laL l l de lL l L de L la de l lL de l l l l lL de la la la deL laL de l L laL la de la l la la lL l de deL de de la l L la lL la de l l de deL la de l l de l L de l l la de deL l lL l laL laL l l l l l la de l l lL laL l de l la l de la de de deL de l l l l la l l lL l de deL lLL l lL la la la l l l lL la de deL de laL la de laL lL de de de l la l l de l de l laL l l L l la l lL de l de la laLL l l l l lL l L l l L de de de L lL de de deL de de la de l lL de l de l la deL l l L de de la de l LL l l la la l la de l l l de l l la la l L de de L l de laL la de de la l l L l l l deL lL de la la L lL la l l de l l l l la l laL de l l l lL l LL l la la de l l de l l l l l l l l la de la la de l L l de la l de la de de l laL de de la de de deL de l laLL l la deL la l de l l la la la l deL deL la de la la la de LLL de de l de de L de la laL l l l l l la la la de L la l de l L l de de la l l L l la la la de la deL deL l l de l la la de l l de la l de de l la l de la de la de lL l de laL l de la la la l de l la l de deL la la de la la de de L de l laL la la de l la l lL la L laL laL l de la l l la la de l l l la l lL l L lL de de l deL la l l la de deL de l de l deL l laL l L de de l l l L l la la l l de de l deL de l la de de l l l de l de la lL l la lLLL de la la deL la l la l de l l l lL l de de l l l l la L laL deL l deL la de l l de deL la l de de l de deL lL de l de la laL l de laL lLL de lL l L l de L la de l de l de de de L l l de L de l de la de L l de deL de l de lL l de la de deL l la L la de la l la l la deL la la l l la la lL la L deL la l de l de de la laLL la de laL l de de la deL de de de l deL l l de de laL lL l l deL la laLL lL de l l l lL l l L de lLL deL laL la deLL la laL de l la l L lL la l deL de deL deL lL de de l la l l lL l l de l de LL l de de l laL la deL de l l l l lL deL deL deL l deL la de L de l de l lL la l lL laLL l l L la L de lL de l de de la de de deLL de l lL l laL l de de de l l L de l L deL de de la la la de de de L deL l laL la l la la la l deL l L l de LL la de la de L l l la lLL la la lL l l L de L de de de la laL la L de de L deL laL l l L l de lL l la l la laL l L de de lL laL deLL la L de de la L l de l laL lL l de l lL de la l de la deLL lL deL la la de la de laL la laL la l la L l deL l de la L l de de la l l l lL de lLL lL l l lL l laL de la l L de l l l de l l de de l l l l L l l de la L deL l de de l l de la la la l l de la la l L la de l l l de lL laL la de la de la l l L lLL de de L lL de deL l de l l la deL l L de lL de la l la l L de L lL l de de l de la L de la l de la de de de lL l laL de L l la l l de l l la l la l L l l l la de lLL l laL la la l lL l de l de l de l laL la l deL la la de de l la L l l l lL de de lL de de l L de laLL l lL de l la la L l de lL la laL l l de lL deL de lLL de lL de de la L de la l de lL l l lL lL lL l la la L l la laL l la L lLL lL la deL de de de de de lL lL la l la de l L l de de de lL l la de la lLL l lL l l L la L la L l L lL lL de de l de de L l L l de l de l deL la L de l la de laL lL de l l l de l l l lL de L l L la de de LL de LL de l la la l de l de la la L deL la L de l l de laL deL l L l de laL l la de la de de deLL l la lL de l L de la la l l deL la L l la de de L l de l deL la l la de lL la de de laLL la L deL de deL de de lLL de lL l L la L l l deL de l L l L lL l L de l de de de la l lL l deL laL l la deLL l l L de l la la de la de deL lL deL l L la de l la L l l l laL l l de de la la deL l de L de l l la la de L laL la l l de de L lL de laL l la L de la la l l l L de L de de de de la lL la la lL lL deL deLL l de de l l l l L laL la l L l l la deL de de l la l l deL laLL de laL deL l L l lL l la la l l laL de l la L l lL la l de l l l l la L la la de la la la la deL la l L de deL de de lL lLL de la l de de de l l l de l l la l L l lL lL la deL deL de l de l L la de l la la de la la de laL de L la L l l l deL de deL la l l l la de l l de l L de de deL l la L deL la la la l l l lL la l laL l la de la l la deL l LLLL l la l de l l de deL l l de la la la de lLLL la de l la de de l de l l la L l L l l deL la l deLL l L l de l L l de L lL laL laL deL de la L l l L de lLL la la la L l de la la l l la l la de de l deL de de de l l laL de de LL lL la LL la l deL de de la la l la la l l L l la de deL la l de L l l la l L la de L l l l L laL de L de deL l l la deL laL lL l l deL l laL l l L lL l de la L de l l L la laL l la l la la l la l de laLL la la deL de de de l lL l L la lL l l la laL l L de l l l L de laL l de de la l l de la la la laL l lL laL de laL de la de l l l la de l l l la de laL de de la laL l l LL deL l l LL la l la l LL de l l la de LL de deL laL la L lL laL la L lL l l L de de laL la lL deL la de de de de lL de l lL la la L de deLL deL de l L de l l l l de L lL laL la de deL de la L l l de L la l l L deL lL l lL de L de l L l L l l l lL l l l l L la l l de la L de l l l l de l L l l l de laL de la L l lL la laL l la de laL l l la l l la de la l la la la de l l deL lLL deL la l la deL la l de de l l l de L la l l L laL de la lLL deL laLL de la deL lL la L l de la de L de L la de de l la de la l l la LL lL laL la laL de de la la L de L l lL de de l de l la la l L de L de l l l la L de la l la de LL la la de l de la L l de L laL l lL de l la laL la l l L de de l LL deL l L l la laL de l l de la lL laL laL de laLL lL de L de de la la l de la la de lLLL l laL la la la de l la de de de L la la la deL l l lL de lL l la L la L de de l l la la l l l lL l de de de la de la LL de lL l la L de l L laL deLL l de LL lL deLL la l de laL de l l laLLL deLL l l la la l la lL de lL l l l de lL l la L l de la l deL de deL lL la de la l de L la lL de L l la l laL la la de L l l L l lL l l deL deL l lL l l L la l LL de laL la deLL la la la de l l de la L deL l L l lL l l la deL de lL de l l L la de la de l L de L l l la de laL de de la de de LL laLL de l la de de la de lLL la laL l l de l lL l de lL l la de l de l LL laLL la de de de lL lL de de la la de de lL la lL l de l de deL l de de de la LL l la de laL l l de la l de l lL de la de de de l la de la la la de L de l de de de de de la l l la lL l l de de lL la l de lL lL de de L l de la la deL l de de lL l L deL de l la de deL de la la la l l laL de l la de l l deL deL laL l de la deL lL l L l de la laL l LL la L l l deL l l l deL la de de l de lLL deL la lL l l de de lL l l de la de l de L de l la de L l la deL l l lL lL la de la de lL la de la de l L de l de L la lL la de de la L laLL l la la l LL laL laL la deL de L de l laL l de de l de l la la L l de de l la la de la de l lL de deL de L l de l la l l l de l de LL la de L l de la la de L l l l l l lL la LL de L l LLL de de l de de de laL de la l L de l de la de laL laL l L la L deL la la laL l l la la de la la LLL lL la l de la la de l l l de l la deLLL laL laLL de la lL laLL de laL la la de laL la de lL l l l la l de de laL la L l lL la de la l lLL de l la la l la de laL la l deL laL laL l l LLLL deL de la l de l de la laL de l de la l LL l la la deL l l de l l de de l de deLLL l l L la de L l laL lL de l l l l de L l l de l la laL l l L l L l la l l lL lL la l l de laL la LL laL lLL de L de l la l de la de L deL l laL la lL la de l l lL la laL de l l L l l l l la la l de L l l l L la la de la l la l L la la l la de l lL de de la la l deL deL de la laL lLL la l l lL de l de la L l de L de l l l de l de L de LL lL la l l lL l l de de la la L l de l deL l la l l laL deL la lLLL de l de L de l de de deL l de L de l L de l deL de L la lL l la L laL l la laL la de de l LLL de la l l l laL laL laL de l deLL la L de la la la LL la de de la la de la lL l de la l l de de LL l l de la l l lLL lL de lL l de l de la la la lL de l laL la la lL la de l lL lL l de laL de de de de de l l L de de l de la L de deL lL l laL l l l l l de l L la deLL la la l la lL la laL de de la de de L de la deL l l L deL de de l l l deL deLL l l l l L l lL de L lL l la l lL de l l lL la l de lL de l deL de l L l L de de la l la L de la lL la lL laL l l de de laL l l la lL l l L la laL l l la la l l L la de laL de L l l L l de la de la l la la lL la l deL l la l l laL de lL l l de l L de la L de de de deL de la de la de la l l de la de de L l de la l L la de l de de l L la laLLL laL lL de de laLL l laL de la l l l L lL l laL la laL lL l l lLLL de L de de la l lL de LL de LL la l de de LL de l l lL l de la la L l de la L l lL l l la la l L l L de deL la l de de lLL la de L de l la lL de LL de de de la L la de laL l l de de l l l deLL la l laL l l L la de de la la L de de l la L de l LL l l la l l L l lL la de l la l l lL la l la LL de L laL l de la de L de lL la l lLL la L l l laL l de l deL laL l la l l la lL l la de LL la la l de l l lL l de de l L l de deL la l la l l deL lL deLL de l l laLL l de l de l l l la la l de de de laL laL de la la L la lLL l de lL la lLL la L l deL laL laL la la laL lLLL de la L la la la L de de l de la de laL l la l L la l de laL l l de la l l l la la l de la la de l lL de la LL la de L l l de de LLL la de L l L l l de l l la laL de l la l de la la l la deL la de la de de l l L de la l l l L laL de L l L la de L la l laL de de de L l laL deL lL deL l l l la la l de L l de la laL l l lLL de de L de deL la lL l deL de l lL de la de laL deL la l la l de l l la L la de l de la L l l de deL la deL deL l de LL la la l l deL la deL la laL deL l lL de l l de la L de la L de l la L de la L de de deL de deLL laLL la l L de de laL deL la l l l l l de laL de l de de l l deL l lLL l laL lL de deL l L l l l la la la de l la laL la l laL laL l L l l de la l de l L de de l de l lL de la de LL l l laL la l l L la de deL deL l deL l de l de L l LL laLLL de l la l de deL l laL de l L la la la de de la la de L la de la l de l de l L l de la de l l L laL la de la de lL l de la lL la laLL laL l L lL de l de de l l deL l la l de l l l la la l la l la l la L l l de l l l l la de de la la de de de la deL la lL la l l deL laLLL de deL l l la de l l deL de la l de lL l L l l L la deL l de l la l l laLL l L de de la de l de l la L l de l de deLL la lL de de laLL la de de de l de lLL de de l l la lL de l la l de de la l l de la lL de l de l l l l L l de la de l laL l la LLL de l lLL lL de laL l l l la de de de lL de laL lL laL de l LLL laL lL l l de de l de l L l L de lLL l l l L la la la la L l de L de l L l l de la l l la de lLL l L de la de l de l la la deL la l de deL la LL laL la l l l lL la la la L l l l de deL l l l l L lL l l laL de de L la de la deL de l de l la de la la deL la de deLL lL de l l lL l L lLL la l l L laL laL la l l la de l de l de l L la deL l L la la de la deLL la l la la de deL deL deL deL l L la l L l l L lL l la de l de l LL laLL la de l l de de de l l de la laL de l la laL l la lL l deL lL deL lL l LL deL de l l la la l l deL la l deL deL l L l de L laLL la l l l deL l l deL deL deL l la L l la deL laL l l la de l de l la l l l l de L de la l la la L l L de deL de la deL la l L l l la de de L l L lL la l de de la l deL de de de la L lL l la l l deL la l la L l L la l de la l laL de l de l L la de lL l l de la de l l L deLL l de l l la L l de l la l lL la L laL la LL de L l la l de de de la l la deL laLL lL l la la lL la de de deL lL de de la L de l l deL de de lL la de de la LL la de la l de de de lL lL la de L laL de de de la L de la de de L de lLL la la deL l L de l laL la L de de la de l l LL l L la la la lLL de L l de la l de la de de l l de l l lL de de de lL laL lL l la la l l de de l L la l de l l deL de l la la de L la l LL laLL la LLL la laL la de de de de l l l l laL la la l la l deL la l de deLL l l la de la la L la l de deL deL deL l de de de l l la la l de de la la L l L l L la de l l de l la l de l L l l de lL l de l de de L l lL la la l l lL la l la L de la l de de L l l deL lL la la de deL la l de de LL deL l l l la de l L la lLLL lLL de L de la de de de l L deL l de la de de lLL de L de l de l la deL deL l LL la de de laL lL l la l la l l l lL la l de de de la L de de deLL la de L de la l l LLL de L de l L la de laL la L la l L l de lLL de de de l la l l la lL laL de l LL de lL la la deL l de l L de laL laL la L de laL l L de de L la de de la lL l l la la lLLL de l la lL l laL deL l l la de la l L la la l L lL de l l de laL l de lL l la L la la la L laL l l l L de l l l de deL de deL de LL la de l de la l de laL la de la l la l de l deL la de l L l LL l la LL de la L de lL l l de laL l l la la lL la la LLL l laL l de de laL de deLL l la la lL l l de L deL la l la L l l L l de l lL l deL de de de de l lL de lLLLL la de L deL l deL la l lLL de de lL de LL laL l l l l L la l la la de l LL deL la l la l la l la deL lL la de de LLL de l l L la l L de la l la l LL l de la lL l l l L l la l LL de la L l lL la l l lL l l la l de de l la de L de lL l de de l l l L l la la de L l la lL lL de la laL de de de l L de l l L laL l lLL l L de la l laL l de l la de L l la LL laL la l de l laL l l l l L laL l L la l laLL la l la l la de laLL l l L la l la l la deL la LL l de L de l l de LLL l l l l l deL laL la deL l de l lL deL l l de laL laL la de laL de deL laL lL lL la deL l la deL la deL la de de l l L de de L de l l l deL de l LLL de l de la l l deL deL l l l la de l l lL l l l la L l laL l l l laL de L l la lL l deL la la l L de deL l lLL la de lL lL lLLL l l lLL l de deL l de deL la LL de de deL de la l lL de l l L l l lLL la de l L l l l lL laL laL la de L l la l l la lL la de LL l de la l l l la l l de lL l deL l lL la de de l L deL laLLL deL de la la l l de de L l L la la l l deL la LL la l l l de l L deL de la l lL la de l de LL de L de l la l lL laL l de la l deL l l la L lL la L lL la de l L la laL l LL l de l l L la la deL l l de de l de LL l la L deL deL la l la la la de L l L l L la de de l l l l l L de la de la la L l l L l de l L l laLL l la de de de deLL de LL lL l l LL lLL l l la l l de lL la deL lL de l l de l deL l de laL de la l L l deLL de l LL la de laL de l L de de lLL l L lL de de LL laLL laL la de de L de laL laL l L la la l L deL de l l la l deL la l l l l l la l l l l LL laL deL de lL de LL de deL laLL l l L l deL de laL l l l L de deL de l l laL la de de l de l la de de lLL de l de l lL deL la LL lL la de l la l l la de la l la de la L de de de deL deLL de deL de l de lL l l la la de l l la L l lLLL de la la la l deL l de L de la la la de l la de la l l la la l L lLL de L lL de l lLLL de l lL l deL laL de la L de la de de de deL de l de lL laL l laL de L de la L l l l de la LLLL lL l de de de l l de l de lL l de de la deL la laL l la L de deL de la l L l laL l l l la deL l l de lL l de l laL la de la deL l l laL de l l l de L de la l deL l deL lL l l deL l L de l de lL de la l L l l de L l l l LL laL de l l lLL l deLL l la L de de lL de de la deL la la laL deL laL l de la lL de de laLL de lL la l deL la de l l lL de l L la de la l L de L de de l L l l l l l de l lLL lL l la LL de l deL la de LLL l la L de deLL l de la de la lL la la l l de la l L lL l deL deL lL de la lL de de LL la lLL de l l L l laL deL lL l de L la l la de laLL la l de l l l la la la l L l L l de L laL lL de laL l de lL de lL de la deLL l de de LL l deL de l de l l la l de la deL la lL de de la l L deL deL deLL la de lLL la lL l la l la la de L l de l l L la la de l de lL la la deL l de la l L de l de lL lL de la de la L deL la l lL l l l l de l la de de de l de l l l de l l de L l l la la L deL la de l la la L de la de l la l la l LL de la la L deLLL l deL l deLL l L l de l L deL de la la laL l l la la de l de la lL la la la l L l l L de l la deLL de de de l lL l de L la la de de laL de de la L de la L l l l de L l L de de de l lLL de de l L deL la de l l l l l de l de l l la de de l l LL la la l L l l l l laL de de l lL de de l la la la l l lL l LL de l L de lL la la lL la l la laL deL laL de laL laLL l L de laL de la l l la de L l deL deL de de la L l la laL la de l l de LL la de l la L deL de l l l lL l la L la l l l deL la l L l L l de la de la l de l de de lLL l l L l l la deL de deL lL deL l l l la de la de la de deLL la l la l L l la l L l de l l la L de de la l l de la de deL la lL laL de de la de deL laL de la L l L de l de de L l L l de L l l la l de de la lL la l de la l l de L l de la l l de LL la l l de de L l de de de L deL lL laL de la de L de l l lL l L deL la l L de la lL la deL lLL l la l de la L la de l laL laL l de la de L la lL de la de L l la l de lLL la l la l L la l laLL de L l l laL la la l LL de la de l de l lL la la L la L l la de l l la L de la l de l L l l l L l laL de laL la deL l la de lL l de laL la l lL l lL l l lL la l de l lL lL la de la l la de la l de de l LL la l l de L de l de de L la de laL l l lL de l de l l de laL lL l de l deL la de la L la deL de de l l deL la la de LL l la de lL de la l lL l la de lL l de la la L de deL l deL la l L la LL de la l l la de l de l la la de deLL l l la l L de l L laL lL l l lL l lL de de l l l la de de de l l la L de de lL l la L l de l L de l l de l l l l de L de de l de lL la la la de la de l L de l la lLL l de la de l l l L l la lLL lL la L la lL l de l l l laL de la L la deL l de lLLL la la de la de deL la de la de de l de laL la l L l de L la de la L de la l la L lLL l laLL de l l de de l l L l l l deLL la deL de l la l l de de lL l de laL l l l lL de l la LL de l lL de L de la la l lL la l l L la l de de l l de la L la l la l de l la la lL l L deL de l la de de lL l de L lL deL de lL deLL la L la L l l la lL l l la L la l la deL lL de l l la de LL lL l la l LL l de l de de l l L l deL laL lL l de l de de de laL la l de de LL l de l de l deLL de l l de la deL la de l L l la LL l LLL lL de lLL de laL la de de de L l l l de l lL lLL la L de deL l l lL deL de l LL l de L l l laL l l l la LL de la L deL l lL de laL de la de de de la de lL de L l L l LLL la l de la de la de LLL l deL de de LLLL de l la L l la LL lL de la l l la la L l la la de laL l deL la la la l l de deL laL de l L la l de la laL de de la L de de L deL lL laL de la l la la lL de l laLL l de l la l l de l de laL de de LL l la la lL la l la l l de de l de l l la l la de L de de la l L l l la l de de l l l l l lL l l la la L deL lL de la l L lL de l la de la de la deL lL l l de la L la L de lL l deL l la l L l l lL l l la la de la de la de L l de de l L de laL laL la l de l la l la de L deL la de l deL lL deL de la de la de de l L deL la de la l la l l l de L de de l L l l de la deL de de l de L la L la l de la deL de de la la deL deL laL la la de de l l L l lL la de de la la de L l la deL de laL l la l LL de de l laL l lL de l l la L deLL la de l la l l laL l de deL la l lL de de L de l l lL de L l la L l l la de L deL de l l la l L deL l l l l L la l l de la la la l la la deL la LL de l deL de deL la la la de l L de l l la la l de lL de l l la l laL l lL l deL l lL l de de l lLL l la L la la l L deL la la la l de l l de la L lL la l de l laL de l LL l la la l de de l L l deL lLL l lL de l lL de de de L l L l deL laL de l L de deL la la lL l de l LL de la lL l l l laL de la l lL l de de de la de laLL l lL l de la la lL de l L de l l l deL de l la lL lL l de la la de l L l LL laL la L deLL l L laL de de l l lL de l l l la l L de laLL l L l la de L de l L l L l la de l LL de la L la lL la l la la lL la de de de lL de deL laL l la l l laL la L l de L la l L de deL l lL laL deL l lL l l laL lL de de de l de l de lLL la deL la de L de la deL l l l de l la la de l la l de la la l de la L la l l la L la lL de L la de de la la l l deL la l la de L l de l lL deL deL la L de deLL lL l de l laL la lL de de L l la l lLL la la lL de l L l de L l de LLL la laLL de lL la L la l de L lL de la l laLLL de lLLLL lL de l L de de l l lL la L l l de l laLL deL de l de lLL la lL de l L de l L de de LLL la l la L la L deL l la L l L de deL la deL de laL l laLL lL lL de la de laL de deL lLLL lL de deL la de l l L l lL l la de lL de L l L de LLL la L l la de la L deL l la L l de lL de de de de l de L la L l deL l de la la LLLL de l deL laLL la l l lL la deL la LL de de la l de de de L de deLL la de de L l L l de de lL l la L l de laL deL l laL de LLL l la L la L lL de de de l L l de l lL de laL lL l l de de la de la de l L lL l l L la LL de l la l L l L l de lL de la l deL lL l L lL deL deL la la l l L l la de deLL l de L de l l de L l la l l laL de lL de la la de la l de l deL l la de l la l laLL deL l de laL l deL l la L l l la la l deLL l LL deLL laL laL l l de la l l lL l de L l la l l l l de de laL la la l laL lL de lL laL de L la de de L de l de la de l l l de L la la lL de la la la l de de la l de L l de de L lL de l la deLL l la de la deLL l L la lL l de de l LLL lL laL de l l laL de l l deL la de L l la la deL de l l l de l l de de de l de LL de l LL de la la la de L l de de de de l la L la L de de la la de la la l l l de l de la l L de L de de l L la de de l l lL la l LL deL
Looks like the chat template fix only addresses turn-based conversations and tool use, which doesn't apply in my usage (which is a single huge prompt --> response with thinking). The above examples are from the content output (post-thinking).