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News/Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation
arxiv
PublishedJune 3, 2026 at 4:00 AM
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Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation

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arxiv.orgfull article ↗
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Publisher summary· verbatim

arXiv:2606.02684v1 Announce Type: cross Abstract: On-Policy distillation (OPD) in large language models is shifting from full-trace KL supervision toward more selective training paradigms. Recent OPD methods increasingly focus on selecting which trajectories to learn from, which tokens are most info

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Discussion
Mentioned models
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  • 01
    FiRe-OPD
Source
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arxiv
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Tags
04
#on-policy#distillation#optimization#language-models

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Mentioned models
01
  • 01
    FiRe-OPD
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#on-policy#distillation#optimization#language-models

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arxivWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems16harxivQuery-Driven Learning for Lifted Reasoning16h
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