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ARXIV:2605.12652 · LLM DISTILLATION · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.12652LLM DISTILLATIONSUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHWeichen Yu · Xiaomin Li · Yizhou Zhao · Xiaoze Liu · Ruowang Zhang · Haixin Wang · +5 at arXiv
MOPD is a peer-conditioned distillation framework for LLMs that leverages successful and failed rollouts to create more informative teacher signals.
Opportunity summary
Pain MOPD is a peer-conditioned distillation framework for LLMs that leverages successful and failed rollouts to create more informative teacher signals.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
MOPD is a peer-conditioned distillation framework for LLMs that leverages successful and failed rollouts to create more informative teacher signals. On-policy distillation (OPD) offers denser token-level supervision by training on student-generated trajectories, yet existing…
Large language models are often post-trained with sparse verifier rewards, which indicate whether a sampled trajectory succeeds but provide limited guidance about where reasoning succeeds or fails. On-policy distillation (OPD) offers denser token-level supervision…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on competitive programming, mathematical reasoning, scientific question answering, and tool-use benchmarks show that MOPD consistently improves over standard on-policy baselines. A public repository…
LLM Distillation moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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MOPD is a peer-conditioned distillation framework for LLMs that leverages successful and failed rollouts to create more informative teacher signals.
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10.48550/arXiv.2605.12652MOPD is a peer-conditioned distillation framework for LLMs that leverages successful and failed rollouts to create more informative teacher signals.
Abstract
Large language models are often post-trained with sparse verifier rewards, which indicate whether a sampled trajectory succeeds but provide limited guidance about where reasoning succeeds or fails. On-policy distillation (OPD) offers denser token-level supervision by training on student-generated trajectories, yet existing methods typically distill each rollout independently and ignore the other attempts sampled for the same prompt. We introduce Multi-Rollout On-Policy Distillation (MOPD), a peer-conditioned distillation framework that uses the student's local rollout group to construct more informative teacher signals. MOPD conditions the teacher on both successful and failed peer rollouts: successes provide positive evidence for valid reasoning patterns, while failures provide structured negative evidence about plausible mistakes to avoid. We study two peer-context constructions: positive peer imitation and contrastive success-failure conditioning. Experiments on competitive programming, mathematical reasoning, scientific question answering, and tool-use benchmarks show that MOPD consistently improves over standard on-policy baselines. Further teacher-signal analysis shows that mixed success-failure contexts better align teacher scores with verifier rewards, indicating that the gains arise from more faithful, instance-adaptive supervision. These results indicate that effective on-policy distillation should exploit the student's multi-rollout trial-and-error behavior rather than treating rollouts as isolated samples.
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PROBLEM
MOPD is a peer-conditioned distillation framework for LLMs that leverages successful and failed rollouts to create more informative teacher signals. On-policy distillation (OPD) offers denser token-level supervision by training on student-generated trajectories, yet existing met...
METHOD
Large language models are often post-trained with sparse verifier rewards, which indicate whether a sampled trajectory succeeds but provide limited guidance about where reasoning succeeds or fails. On-policy distillation (OPD) offers denser token-level supervision by training on...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on competitive programming, mathematical reasoning, scientific question answering, and tool-use benchmarks show that MOPD consistently improves over standard on-policy baselines. A public repo...
WHY NOW
LLM Distillation moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
MOPD is a peer-conditioned distillation framework for LLMs that leverages successful and failed rollouts to create more informative teacher signals. On-policy distillation (OPD) offers denser token-level supervision by training on student-generated trajectories, yet existing methods typically distill each rollout independently and ignore the other attempts sampled for the same prompt.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models are often post-trained with sparse verifier rewards, which indicate whether a sampled trajectory succeeds but provide limited guidance about where reasoning succeeds or fails. On-policy distillation (OPD) offers denser token-level supervision by training on student-generated trajectories, yet existing methods typically distill each rollout independently and ignore the other attempts sampled for the same prompt.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on competitive programming, mathematical reasoning, scientific question answering, and tool-use benchmarks show that MOPD consistently improves over standard on-policy baselines. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Distillation moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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MOPD is a peer-conditioned distillation framework for LLMs that leverages successful and failed rollouts to create more informative teacher signals.
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LLM Distillation
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