Huang, Xuliang, Zhao, Tianxiang, Qin, Chengwei, Shu, Yao · Nov 4, 2026
Xuliang Huang and coauthors propose GIST for selecting offline RLVR training data
Markdown · JSON · Original source
Authors Xuliang Huang, Tianxiang Zhao, Chengwei Qin, and Yao Shu argue that offline with verifiable rewards (RLVR) should not supervise every response and token equally. Their analysis uses the Gibbs-optimal policy of KL-regularized RLVR to identify where a policy differs from its training target: correct responses matter mainly through low-probability tokens, while incorrect responses matter through frequently generated responses and their high-probability tokens. They propose GIST, a two-level data-selection method that uses sampling temperature to select correct responses on-policy and incorrect responses near the policy's mode, then uses initial token probabilities to select tokens for supervision. The authors report higher average accuracy across GSM8K, MATH-500, and MBPP for all three models under two offline training objectives. In a controlled Qwen3-0.6B/GSM8K comparison, they report that GIST outperforms seven selection baselines using the same candidates while supervising 66.6% fewer tokens than training on all candidates of the same prompts. The reported results concern these evaluations; whether the gains and token savings carry over to other settings remains open.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Nov 4, 2026 (2026-11-04).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-09T11:09:58.800Z. First observed: 2026-11-05T05:00:31.762Z. Retrieved: 2026-11-09T11:09:13.740Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The evidence supplied is the abstract, which does not identify the three models or name the two offline training objectives. The 66.6% token reduction and comparison with seven baselines are reported for Qwen3-0.6B/GSM8K, not for every evaluation described.
Open question
Do GIST's reported accuracy gains and token savings hold beyond the models, objectives, tasks, and controlled comparison described in the abstract?
Source-stated
The authors analyze which responses and tokens contribute to mismatch between a policy and the Gibbs-optimal policy of KL-regularized RLVR.
measure the mismatch between a policy and this target with a bidirectional KL divergence
Source-stated
The authors report that correct and incorrect responses contribute to the mismatch through different tokens and response patterns.
correct responses contribute mainly through the forward term, concentrated on their low-probability tokens
Source-stated
GIST uses sampling temperature and initial policy token probabilities to select responses and tokens for supervision before training.
GIST uses the sampling temperature to select correct responses on-policy and incorrect responses near the policy's mode
Source-stated
The authors report higher average accuracy across three benchmarks for all three models under both offline training objectives.
GIST raises the average accuracy over GSM8K, MATH-500, and MBPP for all three models under both objectives.
Source-stated
In one controlled comparison, the authors report that GIST beats seven selection baselines while supervising fewer tokens.
it outperforms seven selection baselines that choose from the same candidates while supervising 66.6% fewer tokens
Source and contributor context
Huang, Xuliang, Zhao, Tianxiang, Qin, Chengwei, Shu, Yao · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Reinforcement learning
Training by trial and error, in which a system is rewarded for the outcomes its designers want.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 98932b57-4924-474d-82eb-ae8c8b0b9b8b.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-09T11:09:58.800Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 2584d49706550c5bd850a3a235fee5c97e4650bcdcd62e10ad4140e3e330204a. Source text hash: 908effa00d9bcf381f00d38ac11a3b8e9a6e6fababa08da6698dcb9e9fc67a33.
Revision and source-check history
Version 1 · 2026-11-09T11:09:58.800Z
Xuliang Huang and coauthors propose GIST for selecting offline RLVR training data
Authors Xuliang Huang, Tianxiang Zhao, Chengwei Qin, and Yao Shu argue that offline with verifiable rewards (RLVR) should not supervise every response and token equally. Their analysis uses the Gibbs-optimal policy of KL-regularized RLVR to identify where a policy differs from its training target: correct responses matter mainly through low-probability tokens, while incorrect responses matter through frequently generated responses and their high-probability tokens. They propose GIST, a two-level data-selection method that uses sampling temperature to select correct responses on-policy and incorrect responses near the policy's mode, then uses initial token probabilities to select tokens for supervision. The authors report higher average accuracy across GSM8K, MATH-500, and MBPP for all three models under two offline training objectives. In a controlled Qwen3-0.6B/GSM8K comparison, they report that GIST outperforms seven selection baselines using the same candidates while supervising 66.6% fewer tokens than training on all candidates of the same prompts. The reported results concern these evaluations; whether the gains and token savings carry over to other settings remains open.
Original source
cs.AI · Article
Source details
Original source date: Nov 4, 2026 (2026-11-04).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-09T11:09:58.800Z. First observed: 2026-11-05T05:00:31.762Z. Retrieved: 2026-11-09T11:09:13.740Z.
Limits
The evidence supplied is the abstract, which does not identify the three models or name the two offline training objectives. The 66.6% token reduction and comparison with seven baselines are reported for Qwen3-0.6B/GSM8K, not for every evaluation described.
Open question
Do GIST's reported accuracy gains and token savings hold beyond the models, objectives, tasks, and controlled comparison described in the abstract?
Source-stated
The authors analyze which responses and tokens contribute to mismatch between a policy and the Gibbs-optimal policy of KL-regularized RLVR.
measure the mismatch between a policy and this target with a bidirectional KL divergence
Source-stated
The authors report that correct and incorrect responses contribute to the mismatch through different tokens and response patterns.
correct responses contribute mainly through the forward term, concentrated on their low-probability tokens
Source-stated
GIST uses sampling temperature and initial policy token probabilities to select responses and tokens for supervision before training.
GIST uses the sampling temperature to select correct responses on-policy and incorrect responses near the policy's mode
Source-stated
The authors report higher average accuracy across three benchmarks for all three models under both offline training objectives.
GIST raises the average accuracy over GSM8K, MATH-500, and MBPP for all three models under both objectives.
Source-stated
In one controlled comparison, the authors report that GIST beats seven selection baselines while supervising fewer tokens.
it outperforms seven selection baselines that choose from the same candidates while supervising 66.6% fewer tokens
Source and contributor context
Huang, Xuliang, Zhao, Tianxiang, Qin, Chengwei, Shu, Yao · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Reinforcement learning
Training by trial and error, in which a system is rewarded for the outcomes its designers want.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 98932b57-4924-474d-82eb-ae8c8b0b9b8b.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-09T11:09:58.800Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 2584d49706550c5bd850a3a235fee5c97e4650bcdcd62e10ad4140e3e330204a. Source text hash: 908effa00d9bcf381f00d38ac11a3b8e9a6e6fababa08da6698dcb9e9fc67a33.
uncertain · 2026-11-09T11:09:58.800Z · anthropic/claude-opus-5.5
Publication: 98932b57-4924-474d-82eb-ae8c8b0b9b8b
attribution — pass: The reading credits the paper and its findings to the four listed arXiv authors and frames the results as author reports. This matches the source.
Authors:Xuliang Huang, Tianxiang Zhao, Chengwei Qin, Yao Shu
summary — uncertain: The summary follows the abstract, but its core is unexplained jargon: Gibbs-optimal policy, KL-regularized, policy, tokens, on-policy, policy's mode, and the benchmarks GSM8K, MATH-500 and MBPP. RLVR is spelled out but never described in plain terms. A general reader cannot tell what changed or why fewer supervised tokens might matter. It also drops two scope conditions: the analysis holds exactly only at the reference policy, and GIST approximates those principles. Smallest repairs: add one plain sentence on what RLVR training is, add tooltip entries for the listed terms, and restore 'at the reference policy' and 'approximates'.
At the reference policy, this mismatch factorizes exactly and separates the two outcomes
date — pass: The originalPublished date of 2026-11-04 matches the arXiv v1 submission date. The feed date of Nov 5 is a later announcement.
[Submitted on 4 Nov 2026]
support — pass: Each claim matches the quoted abstract text. The 66.6% figure is correctly scoped to the Qwen3-0.6B/GSM8K comparison. The limitations correctly note that the three models and two objectives are not named. The open question does not overstate the results.
In a controlled comparison on Qwen3-0.6B/GSM8K, it outperforms seven selection baselines that choose from the same candidates while supervising 66.6% fewer tokens
media — pass: The selected media is the verified direct arXiv abstract page for this paper, which is the most direct way to reach the source.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
Training by trial and error, in which a system is rewarded for the outcomes its designers want.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivTraining by trial and error, in which a system is rewarded for the outcomes its designers want.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv