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arXiv Machine Learning By Ning Liu, Ian Li, Kristina Klinkner

SynAct: Synergistic Active Feedback Learning for LLM Agents

Read the original on arXiv Machine Learning →

SynAct is an active learning framework that allocates scarce expert feedback to individual decisions in large language model agents, ranking decisions by the mutual information between the chosen action and the final outcome across repeated rollouts. By querying experts at the highest‑value decisions and distilling corrections into reusable rules, SynAct improves held‑out accuracy by an average of 11.7 percentage points across 16 settings and outperforms budget‑matched routers in inference‑time routing. The method is theoretically grounded, showing that outcome‑separating decisions have a minimum mutual information of H2(m/k) bits, enabling exact separation for any threshold within that range.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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