# A policy-compatible approach to reasoning when model attempts fail [Canonical reading](https://escapemeridian.com/readings/a-policy-compatible-approach-to-reasoning-when-model-attempts-54e41d06) Authors Zehao Wang, Hanzhu Chen, Runquan Gui, Zhe Wang and Lin Yang argue that reinforcement learning 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 benchmarks. 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 arXiv cs.AI · Article 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. - [When All Rollouts Fail: Policy-Compatible Joint Decoding for Hard-Problem Reasoning](https://arxiv.org/abs/2610.36849) ## Limits The evidence is an abstract; it does not give performance measurements or name the datasets and benchmarks, 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 benchmarks? ## 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 - [Supporting source](https://arxiv.org/abs/2610.36849) ## 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 - [Supporting source](https://arxiv.org/abs/2610.36849) ## 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. - [Supporting source](https://arxiv.org/abs/2610.36849) ## 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. - [Definition reference](https://developers.google.com/machine-learning/glossary#reinforcement-learning-rl) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## 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 Authors Zehao Wang, Hanzhu Chen, Runquan Gui, Zhe Wang and Lin Yang argue that reinforcement learning 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 benchmarks. The abstract does not give performance measurements or enough detail to assess how broadly those results apply. ## Original source arXiv cs.AI · Article 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. - [When All Rollouts Fail: Policy-Compatible Joint Decoding for Hard-Problem Reasoning](https://arxiv.org/abs/2610.36849) ## Limits The evidence is an abstract; it does not give performance measurements or name the datasets and benchmarks, 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 benchmarks? ## 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 - [Supporting source](https://arxiv.org/abs/2610.36849) ## 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 - [Supporting source](https://arxiv.org/abs/2610.36849) ## 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. - [Supporting source](https://arxiv.org/abs/2610.36849) ## 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. - [Definition reference](https://developers.google.com/machine-learning/glossary#reinforcement-learning-rl) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## 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. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/54e41d067f10a8abb753a9d0dac14feae5ce265ba1935bf89714bd3a151733f0/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/a-policy-compatible-approach-to-reasoning-when-model-attempts-54e41d06.json)