Wang, Zehao, Chen, Hanzhu, Gui, Runquan, Wang, Zhe, Yang, Lin · Oct 27, 2026
A policy-compatible approach to reasoning when model attempts fail
Markdown · JSON · Original source
Authors Zehao Wang, Hanzhu Chen, Runquan Gui, Zhe Wang and Lin Yang argue that can stall on problems a model cannot yet solve: if every attempt fails, binary rewards provide no useful learning signal. They propose Policy-Compatible Reinforcement Learning (PICO), which uses expert data to guide token-by-token generation while preserving the model’s relative token probabilities. The authors say this produces verified trajectories compatible with the model’s policy, which they train alongside ordinary attempts. They report that PICO outperforms standard RLVR and earlier off-policy methods across reasoning datasets and agent . The abstract does not give performance measurements or enough detail to assess how broadly those results apply.
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: Oct 27, 2026 (2026-10-27).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-05T08:52:18.611Z. First observed: 2026-10-28T08:04:31.655Z. Retrieved: 2026-10-30T00:33:11.008Z.
Referenced by
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Limits
The evidence is an abstract; it does not give performance measurements or name the datasets and , so the reported gains cannot be assessed in detail here.
Open question
How large are PICO’s reported gains, and do they hold across the individual datasets and agent ?
Source-stated
The authors say that when every attempt at a hard problem fails, binary rewards provide no useful learning signal for group-relative estimators.
when every rollout fails, the samples reveal nothing about a correct solution, and group-relative estimators (e.g., GRPO, RLOO) collapse the update to zero advantage
Source-stated
The authors propose PICO, which uses expert data to guide token-level decoding while preserving the student model’s relative token probabilities.
expert data guides decoding at the token level while preserving the student's relative token probabilities
Source-stated
The authors report that PICO outperforms standard RLVR and prior off-policy baselines in experiments on reasoning datasets and agent benchmarks.
Experiments across reasoning diverse datasets and agentic benchmarks demonstrate that PICO consistently outperforms standard RLVR and prior off-policy baselines.
Source and contributor context
Wang, Zehao, Chen, Hanzhu, Gui, Runquan, Wang, Zhe, Yang, Lin · Article author
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Source media
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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.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
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: 6941d23b-ab03-4aa4-8296-af634f9a50f7.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-05T08:52:18.611Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 2f1bc7d9db14c6189c513a374f2cc37b550e1460ded0b724861da27f7dff855e. Source text hash: f89f83c8138a40e2e95b24cda647cacd046e68eb4f7f63463bafcf80e4b1daad.
Revision and source-check history
Version 1 · 2026-11-05T08:52:18.611Z
A policy-compatible approach to reasoning when model attempts fail
Authors Zehao Wang, Hanzhu Chen, Runquan Gui, Zhe Wang and Lin Yang argue that can stall on problems a model cannot yet solve: if every attempt fails, binary rewards provide no useful learning signal. They propose Policy-Compatible Reinforcement Learning (PICO), which uses expert data to guide token-by-token generation while preserving the model’s relative token probabilities. The authors say this produces verified trajectories compatible with the model’s policy, which they train alongside ordinary attempts. They report that PICO outperforms standard RLVR and earlier off-policy methods across reasoning datasets and agent . The abstract does not give performance measurements or enough detail to assess how broadly those results apply.
Original source
cs.AI · Article
Source details
Original source date: Oct 27, 2026 (2026-10-27).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-05T08:52:18.611Z. First observed: 2026-10-28T08:04:31.655Z. Retrieved: 2026-10-30T00:33:11.008Z.
Limits
The evidence is an abstract; it does not give performance measurements or name the datasets and , so the reported gains cannot be assessed in detail here.
Open question
How large are PICO’s reported gains, and do they hold across the individual datasets and agent ?
Source-stated
The authors say that when every attempt at a hard problem fails, binary rewards provide no useful learning signal for group-relative estimators.
when every rollout fails, the samples reveal nothing about a correct solution, and group-relative estimators (e.g., GRPO, RLOO) collapse the update to zero advantage
Source-stated
The authors propose PICO, which uses expert data to guide token-level decoding while preserving the student model’s relative token probabilities.
expert data guides decoding at the token level while preserving the student's relative token probabilities
Source-stated
The authors report that PICO outperforms standard RLVR and prior off-policy baselines in experiments on reasoning datasets and agent benchmarks.
Experiments across reasoning diverse datasets and agentic benchmarks demonstrate that PICO consistently outperforms standard RLVR and prior off-policy baselines.
Source and contributor context
Wang, Zehao, Chen, Hanzhu, Gui, Runquan, Wang, Zhe, Yang, Lin · 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.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
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: 6941d23b-ab03-4aa4-8296-af634f9a50f7.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-05T08:52:18.611Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 2f1bc7d9db14c6189c513a374f2cc37b550e1460ded0b724861da27f7dff855e. Source text hash: f89f83c8138a40e2e95b24cda647cacd046e68eb4f7f63463bafcf80e4b1daad.
uncertain · 2026-11-05T08:52:18.611Z · anthropic/claude-opus-5.5
Publication: 6941d23b-ab03-4aa4-8296-af634f9a50f7
attribution — pass: The five named authors match the arXiv listing. The problem diagnosis, the PICO proposal and the performance results are all framed as the authors' arguments or reports.
Authors:Zehao Wang, Hanzhu Chen, Runquan Gui, Zhe Wang, Lin Yang
summary — uncertain: The summary uses RLVR without saying it stands for Reinforcement Learning with Verifiable Rewards. Policy, trajectories and off-policy are also left unexplained for general readers. It omits the authors' central reason for PICO: expert trajectories fall in the student's low-probability regions and decay into surface-level imitation. It also drops consistently from the results claim. Smallest repair: define RLVR and policy, and add one plain sentence on why raw expert data fails.
they are incompatible with the current policy and often lie in the student's low-probability regions, so the surviving updates demand large parameter shifts and decay into surface-level imitation rather than transferable reasoning
date — pass: The displayed original publication date of 2026-10-27 matches the v1 arXiv submission date. The later feed date is not used.
[Submitted on 27 Oct 2026]
support — pass: Each claim is supported by abstract text and presented as an author statement. The limitation and the question correctly note that the abstract gives no numbers and names no benchmarks. No unsupported benefits or costs are implied.
Experiments across reasoning diverse datasets and agentic benchmarks demonstrate that PICO consistently outperforms standard RLVR and prior off-policy baselines.
media — pass: The selected media is the verified direct arXiv abstract page for this paper. It is the most direct access to the source.
Challenge or correct this reading
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Training by trial and error, in which a system is rewarded for the outcomes its designers want.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceTraining by trial and error, in which a system is rewarded for the outcomes its designers want.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition reference