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On-policy distillation post-trains large language model agents by supplying dense, step-level guidance from a teacher policy when the reinforcement-learning reward is sparse and arrives only once per trajectory. Existing instantiations allocate this guidance by the size of the teacher-student divergence at each step, o...
RankBuffer is introduced, which maintains an ordered, query-specific buffer of previously judged responses as a reusable quality scale that achieves nearly on-par performance with the strongest ranking-based reward baseline while substantially reducing judging cost.
Fetch-then-Explore is proposed, which separates page selection from evidence extraction and keeps what it selects: pages are recorded in a per-question workspace on the filesystem rather than the context window or a transient session, and evidence is pulled from them on demand later.
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