Reddit user am17an reports that adding a logit bias against hedging tokens ('wait', 'maybe', 'perhaps') improves quantized Qwen3.5-4B accuracy on a 50-question MATH-500 sample across llama.cpp quantizations, extending a Meta paper's finding to local inference; replication or refutation by other local-inference users would settle whether token-level logit penalties are a practical accuracy knob for quantized models.
state: corroboratedheat: mediumuncertainty: mediumconvergesscott: mediumdecoding-strategies local-inference quantizationam17anMeta
Surfaced 2026-09-29T08:09:07Z — Per the Reddit post's citation, a Meta paper found that penalizing hedging tokens like 'wait', 'maybe', and 'perhaps' improves model accurac — Third consecutive velocity_spike is the same cumulative-score artifact (582 pts vs cohort p90 135), not renewed motion: ~1.2 pts/h and ~0.2 comments/h at 39h age, +3 points/+1 comment since the last look, no new evidence, replications, or periphery expansion — the magnitude-valve 'two platforms' reading still counts echo testimony as its second platform. The case's meaning is unchanged (corroborated phenomenon, still single-source training-free trick), so it holds at corroborated/low; the 79th-percentile residual rate is just cohort decay, not a signal worth hours-level attention.
What is this?
A r/LocalLLaMA post by user am17an claims that adding a logit bias against hedging tokens ('wait', 'maybe', 'perhaps') improves quantized Qwen3.5-4B accuracy on a 50-question MATH-500 sample across llama.cpp quantizations, citing a Meta paper that found penalizing hedging tokens improves model accuracy. The supplied snippets do not surface the post itself and do not independently verify the Meta paper — the paper is attested only through the post's own citation — so the claim currently rests on a single first-person report. What the snippets do confirm is the setting: Qwen3.5-4B is a real, actively discussed local model, and r/LocalLLaMA shows a live culture of exactly this kind of token-level llama.cpp hacking (e.g. a parallel logit-bias trick forcing </think> on Qwen3.5) and quantization-accuracy comparison work. Replication or refutation by other users is not yet in evidence.
Why it matters to Scott
Converges with his effort-control canon: a Meta finding (even if only citation-attested) plus a community replication independently back the position his high-not-max and inference-time-scaling pages already lean on — that hedging/backtracking tokens like 'wait' are a cost center whose suppression can raise accuracy — while adding a distinct training-free sampler-level mechanism beside ukisai's trained approach. It bears directly on technologies he actively runs (gamepc's quantized Ollama/llama.cpp serving), making it a one-evening self-replication with proper eval hygiene rather than mere illustration; the n=50 MATH-500 sample and single first-person report are what hold it at medium.
ip:concept.inference-time-scalingip:concept.high-not-maxdev:project.gamepcdev:concept.hardware-aware-local-inferenceradar:mindcontrol-llamacpp-reasoning-budgetsradar:ukisai-swift-family-releaseradar:ds4-runtime-directional-steeringradar:qwen-tensor-level-quant-allocationradar:concept.llama-cppradar:concept.local-inference
queries asked of Scott's wikis
- logit bias / sampler settings as inference-time accuracy knob
- quantization accuracy degradation and cheap recovery tricks
- reasoning-model backtracking tokens ('wait') as self-correction
- overthinking / reasoning-effort reduction techniques
- small-sample eval methodology (50-question MATH-500, noise)
- llama.cpp decoding knobs and local inference tooling
Measured heat
now 0 pts/hpeak 83 pts/hcomments 0/hpeers p0momentum: steady2 platformsage 336h
points/hour across evidence · reading as of 2026-10-12 02:59:37.977291+11:00 · deterministic, not a model opinion
How the heat travelled
pace: p88 vs 1188 stories at the 168h mark (now 336h old) — ahead of anthropic-biomolecular-model-optimization (1.0x), behind valsai-sonnet55-thomson-lean-proof (1.0x)
Evidence (3) — ⭐ canonical anchor
Interpretation history
2026-10-02T20:00:46Z
The refutation data point the case explicitly awaited arrived: the same −2 hedging-token bias slightly hurt Bonsai 2 27B (44→43/50 on MATH-500, within n=50 noise but no improvement), so the case's meaning shifts from 'promising universal knob awaiting replication' to 'real, corroborated phenomenon with a model-specific knob' — the Qwen3.5-4B result stays single-source and demonstrably does not transfer blindly. Both threads are long cold (~0.33 pts/h at 123h), so heat holds low while the case stays corroborated (community report + Liquid AI's independently identified doom-loop phenomenon and shipped trained fix).
2026-10-02T19:29:57Z
evidence attached: reddit.post.1ww0x53 — Controlled refutation data point the case explicitly awaits: the hedging-token bias slightly hurt accuracy on Bonsai 2 27B (44→43/50), suggesting the knob may be model-specific.
2026-09-29T08:04:22Z
magnitude valve eligible (multi-platform, top-decile engagement) and never alerted; deterministic escalation to deliver
2026-09-28T07:57:26Z
The velocity_spike trigger is a cumulative-score threshold crossing (469 pts vs cohort p90 135), not renewed acceleration: measured rate is ~15 pts/h vs an ~82 pts/h peak, momentum cooling, 103 comments that are amplification and method critique of what the case already knows. Nothing new material — no independent replication of the training-free trick, no new implementations, no spread beyond r/LocalLLaMA (the magnitude-valve 'two platforms' reading still counts echo testimony as its second platform). Cooling heat to low while the case holds at corroborated; the decisive step remains an independent replication.
2026-09-28T03:48:19Z
Liquid AI appeared in-thread claiming prior independent identification of the same hedging-token/doom-loop phenomenon, with a published article and a tool that trains the behavior out — a consequential-participant corroboration that lifts the case from single-report seed to corroborated, while the specific training-free logit-bias-on-quantizations result remains single-source (n=50, math only). Heat is medium, not high: velocity peaked ~82 pts/h at the 98.9th peer percentile but is cooling inside a single community, and the magnitude-valve spread reading counts the echo testimony as a second platform.
2026-09-27T17:37:01Z
grounded: converges/medium — Converges with his effort-control canon: a Meta finding (even if only citation-attested) plus a community replication independently back the position his high-n
2026-09-27T17:28:24Z
case created — A bounded, resolvable first-person replication of a published decoding finding onto the llama.cpp/quantization stack Scott tracks, with no existing case covering inference-time hedging-token penalization (ukisai's trained overthinking reduction is a distinct mechanism and claim).
Decision trace
- 10-04 15:26review_screenjev screen: no material development (noul=0.18)
- 10-03 20:20sensor_dirtycomment_update
- 10-03 06:00repriceThe refutation data point the case explicitly awaited arrived: the same −2 hedging-token bias slightly hurt Bonsai 2 27B (44→43/50 on MATH-500, within n=50 noise but no improvement), so the case'
- 10-03 05:29attachControlled refutation data point the case explicitly awaits: the hedging-token bias slightly hurt accuracy on Bonsai 2 27B (44→43/50), suggesting the knob may be model-specific.
- 10-03 05:23propose_attachControlled refutation data point the case explicitly awaits: the hedging-token bias slightly hurt accuracy on Bonsai 2 27B (44→43/50), suggesting the knob may be model-specific.
- 09-29 18:09pushPer the Reddit post's citation, a Meta paper found that penalizing hedging tokens like 'wait', 'maybe', and 'perhaps' improves model accurac — Third consecutive velo
- 09-29 18:04repriceThird consecutive velocity_spike is the same cumulative-score artifact (582 pts vs cohort p90 135), not renewed motion: ~1.2 pts/h and ~0.2 comments/h at 39h age, +3 points/+1 comment since the last l
- 09-29 18:04alert_heldPer the Reddit post's citation, a Meta paper found that penalizing hedging tokens like 'wait', 'maybe', and 'perhaps' improves model accurac — Third consecutive velo
- 09-29 18:04alert_routePer the Reddit post's citation, a Meta paper found that penalizing hedging tokens like 'wait', 'maybe', and 'perhaps' improves model accurac — Third consecutive velo
- 09-29 07:23sensor_dirtyvelocity_spike
- 09-28 23:21sensor_dirtyvelocity_spike
- 09-28 17:57repriceThe velocity_spike trigger is a cumulative-score threshold crossing (469 pts vs cohort p90 135), not renewed acceleration: measured rate is ~15 pts/h vs an ~82 pts/h peak, momentum cooling, 103 commen
- 09-28 17:21sensor_dirtyvelocity_spike
- 09-28 13:48repriceLiquid AI appeared in-thread claiming prior independent identification of the same hedging-token/doom-loop phenomenon, with a published article and a tool that trains the behavior out — a consequentia
- 09-28 11:20sensor_dirtyvelocity_spike
- 09-28 10:21sensor_dirtycomment_update
- 09-28 04:20sensor_dirtyvelocity_spike
- 09-28 03:37groundConverges with his effort-control canon: a Meta finding (even if only citation-attested) plus a community replication independently back the position his high-not-max and inference-time-scaling pages
- 09-28 03:28createA bounded, resolvable first-person replication of a published decoding finding onto the llama.cpp/quantization stack Scott tracks, with no existing case covering inference-time hedging-token penalizat