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Design only — no implementation, nothing run. cot_sidecar.py is untouched; it is the reference behind the published GPQA 90.37 / HLE 29.19 numbers and should stay byte-identical to what produced them. The existing sidecar compresses after the model stops thinking, which answers "is the trace load-bearing?" but not "can reasoning continue from a compressed state?" — a read versus a write that has to be in-distribution as a prefix. This designs the second: compress every 4096 tokens and keep going. Shape follows InftyThink / Delethink / LightThinker, with one deliberate departure: summaries accumulate rather than replace, so each chunk is compressed exactly once and the compounding-loss failure mode of recursive summarization does not arise. State is then O(N/8) — ~35K against a 262K window, so it does not bind. Because peak context no longer tracks total thinking length, the 1.9n+240 <= 262144 constraint that pinned THINK_BUDGET at 196,608 is gone, and the arm is budget-matched to the no-sidecar baseline for the first time: 58 x 4096 = 237,568 reasoning + 8,192 answer = 245,760 = baseline MAX_NEW. Records the reasoning behind the choices that are not obvious: why the summary is generated as a continuation inside <think> rather than as a separate compression call, why SUMMARY_HINT must not close </think>, why the final chunk is never compressed, and which measured failure each clause of the summary prompt targets. Edge cases are enumerated with honest status rather than a coverage claim — the pass found eight gaps in the first draft of the loop, five marked must-fix for v1, including an EOS/</think> conflation and a None-deref when the context guard fires on chunk 0. Open for review: T_SUMM=0.3 is unmeasured; CONTINUE_HINT is unablated; no progress signal in the state, which is the cue whose removal most threatens the </think> rate. Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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| CONTINUE_HINT = "\n\nPicking up where I left off:\n" | ||
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| SUMMARY_HINT = ( |
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can this be compressed a bit?
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Done in dab759f089 — halved, ~90 words to ~44, all six target clauses kept:
SUMMARY_HINT = (
"\n\nI'm out of space, so I'll stop mid-thought here and note what I need "
"to resume: exact values I've established, what I was partway through, "
"what I've ruled out, and what's still open. Only what's new since my "
"last note.\n\n"
)Kept the clause→failure table so the reason each phrase is there stays recorded, and
added a note on why length matters here specifically: it is re-tokenized into every
summary call, and a long instruction at the seam both costs attention and biases the
model toward list-shaped output rather than a natural note.
| them would handicap the arm against the baseline it is compared to. They still appear | ||
| in `completion_tokens` for verbosity, tracked on a separate line. | ||
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| Worst-case state: 58 x 512 = 29,696. Peak context ~35K against a 262K window. |
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Could you make sure if the reasonings go out of budget, we return the same behavior as the original VLLM server
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Good catch — this was a validity bug, not a cosmetic one. Fixed in dab759f089,
new "Out-of-budget behaviour" section.
The first draft injected </think> at budget exhaustion and generated an answer.
cot_sidecar.py already records the measured baseline behaviour:
"vLLM ... puts an unclosed block in reasoning_content and leaves content empty.
Measured on the uncompressed xhigh baseline: every 245760-token non-terminating
generation came back withgeneration=""andreasoning_content=the whole trace,
so the grader saw an empty answer and simply marked it wrong."
So baseline scores those requests wrong. Manufacturing an answer would have
collected points the baseline never had a chance at, concentrated on exactly the
hardest problems — a confound in this arm's own favour.
Forced-answer path removed. Out of budget now returns content="",
reasoning_content=state+R_n, finish_reason="length", and completion_tokens =
tokens actually generated, reusing cot_sidecar.py's no_think_block return shape so
both compressed arms are byte-comparable at that boundary.
Rebudgeted as a consequence: baseline max_tokens is a single pool shared by reasoning
and answer, so the arm mirrors that instead of pre-splitting it — 60 × 4096 = 245,760
for R blocks, and answer_room = min(ANSWER_BUDGET, 245760 - thinking). Reasoning can
now consume the whole pool exactly as baseline can, and when it does, nothing is left
for an answer and none is generated.
Bonus: this subsumes edge case G2. When the context guard fires before any chunk runs,
R is None, reasoning is the empty state, and the same return fires — no special case,
no None deref. Four must-fix gaps left for v1.
I'll validate this path directly against the previously failed HLE traces before
launching anything.
Two review comments. "can this be compressed a bit?" (SUMMARY_HINT) — halved, ~90 words to ~44, with all six target clauses kept. It is re-tokenized into every summary call, and a long instruction at the seam both costs attention and biases the model toward list-shaped output instead of a natural note. "make sure if the reasonings go out of budget, we return the same behavior as the original VLLM server" — this was a validity bug, not a cosmetic one. The first draft injected </think> at budget exhaustion and generated an answer. cot_sidecar.py already records the measured baseline behaviour: vLLM puts the unclosed block in reasoning_content and leaves content empty, so the grader marks those requests wrong. Manufacturing an answer would have collected points the baseline never had a chance at, concentrated on exactly the hardest problems — a confound in the arm's own favour. The forced-answer path is removed. Out of budget now returns content="", reasoning_content=state+R_n, finish_reason="length", completion_tokens=tokens actually generated, reusing cot_sidecar.py's no_think_block return shape so both compressed arms are byte-comparable at that boundary. Rebudgeted as a consequence. Baseline max_tokens is a single pool shared by reasoning and answer, so the arm mirrors that instead of pre-splitting: 60 x 4096 = 245,760 for R blocks, answer room = min(ANSWER_BUDGET, 245760 - thinking). Reasoning can now consume the whole pool exactly as baseline can, and when it does nothing is left for an answer and none is generated. Also resolves edge case G2 for free: when the guard fires before any chunk runs, R is None, reasoning is the empty state, and the same return fires — no special case, no None deref. Four must-fix gaps remain for v1. Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
cot_online_sidecar.py implements the reviewed design. cot_sidecar.py is untouched. The four v1 gaps are fixed and each has a test: - EOS vs </think>: finish_reason is "stop" for both, so the loop keys off membership of the </think> token in the output (correct whether or not vLLM includes a stop token) and carries the real terminal reason out rather than coercing it to "length". - empty note: if the model closes the block or emits nothing, fall back to R[-FALLBACK_TAIL:] — Delethink-style truncation as a degradation path, so the loop survives instead of splicing a bare label into the notes. - reserved tokens in a note: sanitised before splicing, or the model's own <think> would open a block we do not control. - detok/retok round trip: notes stay token IDs end to end, spliced between pre-tokenized scaffolding. The round trip could change token counts and, on partial UTF-8 boundaries, content. test_online_sidecar.py drives every branch against a scripted fake backend — no GPU, no vLLM. 32 assertions, all passing. test_online_hle_parity.py checks the out-of-budget path against ground truth extracted from the uncompressed xhigh HLE run (hle_truncated_baseline.json). Of 2158 rows, 11 hit finish_reason=length, and every one came back with generation="" (0 chars), reasoning_content of 253K-858K chars, exactly 245760 generated tokens, and symbolic_correct=False. That quantifies the confound the review caught: answering those 11 would have been 0.51 HLE points the baseline had no chance at — roughly 18% of the -2.88 delta measured for offline compression. The arm now forfeits the same 11. One behaviour worth noting: no note is written after the final chunk. It is kept verbatim for the answer phase, so summarising it would be wasted work. Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Two harness-facing fixes, neither touching the loop. httpx defaults to 100 max connections. NEL runs parallelism=128 and every request makes up to 129 sequential sub-calls, so the pool itself would have become the bottleneck. Sized to 512, overridable via MAX_CONNS. n>1 now returns 400 instead of silently returning a single choice, which a pass@k harness would mis-score. NEL sends repeats as separate requests, so this should never fire -- it is there so that if it ever does, the run fails loudly rather than reporting a wrong number. Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Checks what a scripted backend cannot. Run against job 549615 on 8x B200, C_GEN=512 THINK_TOTAL=2048 so chunking and budget exhaustion both trigger in seconds. All pass. Two backend facts it pinned down: - The Qwen3.8 chat template already opens <think>, so think_open must be []. - /inference/v1/generate DOES include the stop token in token_ids. The loop keys off membership of </think> rather than a last-token check, so this is handled either way -- but an implementation that appended </think> after a stop-token hit would have emitted it twice. Also confirmed live: notes accumulate and reasoning genuinely resumes across chunks, and enable_thinking=false passes through byte-for-byte identical to vLLM with no online metadata attached. Worth watching in the real run: at SUMM_CAP=128 all three notes hit the cap. 512 is the configured value and should have headroom, but summary_truncated is a reported metric for exactly this reason. Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Found by inspecting the first real run (fdc7b2c8356e1bb6) after ~45 min; run
killed, results discarded.
5.8% of GPQA and 22.1% of HLE requests were ending with the model emitting EOS
while still inside the think block, at 2-16 chunks and 4K-64K thinking tokens --
nowhere near the 60-chunk budget. Every one carried a complete answer:
...Therefore, the number of carbon atoms in product 3 is 11.
Answer: A<|im_end|>
Resumed mid-thought by CONTINUE_HINT, the model never gets a natural moment to
emit </think>; it writes the answer inline and stops. The first implementation
routed that through unclosed(), returning content="", so every one of those
answers scored zero.
The out-of-budget rule does not apply to it. That rule is validated against
baseline requests that ran out of tokens, which the baseline scores wrong. A
voluntary stop is a different event -- the baseline never scores that wrong,
because its voluntary stop always carries </think> and an answer. Closing the
tag here restores parity rather than breaking it.
The loop now branches three ways: tag emitted, EOS without tag, budget
exhausted. Only the third returns empty content. Trailing specials are stripped
before </think> is spliced, and eos_in_think is reported so the rate stays
auditable -- if it is high, CONTINUE_HINT is the seam to ablate.
test_online_sidecar.py case 4 rewritten for the new behaviour and case 4b added,
asserting the out-of-budget path is byte-identical to before. Both suites pass.
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
First run found a defect; killed, fixed, relaunchedLaunched 5.8% of GPQA and 22.1% of HLE requests were having correct answers discarded. Non-closers maxed out at 16 chunks / 64K thinking tokens — nowhere near the The model finished, wrote its answer, and emitted EOS without ever writing I over-applied your review fixYour comment was about out of budget, and I applied it to every path lacking
The baseline never produces an unclosed voluntary stop, so there was no Fixed in
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1 participant
Design doc only — no implementation, nothing run. Opening as a draft against
cot-compression-sidecar(this fork) for review before any code is written.cot_sidecar.pyis untouched. It is the reference implementation behind thepublished GPQA 90.37 / HLE 29.19 numbers and should stay byte-identical to what
produced them; the online variant will be a separate module.
What this is for
The existing sidecar compresses the trace after the model stops thinking. That
answered "is the trace load-bearing?" — on GPQA no (90.37 vs a 90.50 identity
control while discarding 97.6%), on HLE somewhat (−2.88).
It did not answer "can reasoning continue from a compressed state?" Those are
different capabilities: the first is a read, the second needs the compressed state
to be in-distribution as a prefix the model then writes from. This design tests the
second, compressing every 4096 tokens and continuing.
Shape
Follows InftyThink / Delethink / LightThinker, with one deliberate departure:
summaries accumulate rather than replace, so each chunk is compressed exactly
once and recursive summarization's compounding-loss failure mode does not arise.
State becomes O(N/8) — ~35K against a 262K window, so it does not bind.
R_0…R_(n−1)are discarded once summarized;R_nis kept verbatim for theanswer phase.
Budget is matched to the no-sidecar baseline
Peak context no longer tracks total thinking length, so the
1.9n + 240 ≤ 262144constraint that pinned
THINK_BUDGETat 196,608 is gone. For the first time the armcan match baseline exactly:
Review focus
The doc records rationale for the non-obvious choices — why the summary is a
continuation inside
<think>rather than a separate compression call, whySUMMARY_HINTmust not close</think>, why the final chunk is never compressed,and which measured failure each clause of the summary prompt targets.
Four things I'd particularly like challenged, all listed as open questions:
T_SUMM = 0.3is unmeasured — reasoning runs at 1.0CONTINUE_HINTis four words doing a lot of work at the seam, and unablated</think>rate\n\nboundaryOn edge cases
Enumerated with honest status rather than a coverage claim. The pass found eight
gaps in the first draft of the loop, five marked must-fix for v1 — including an
EOS-vs-
</think>conflation (finish_reasonis"stop"for both) and aNonederef when the context guard fires on chunk 0.
think_closedis listed as a primary metric, not a diagnostic: re-seeding with acompact state removes the growing-context cue that tells a model it has been at this
a while, and if the
</think>rate collapses that is the finding.