arXiv：cs.LG· Ning Liu, Ian Li, Kristina Klinkner·· 11 小时前
SynAct：面向 LLM 智能体的协同主动反馈学习框架
SynAct: Synergistic Active Feedback Learning for LLM Agents
AI 导读
SynAct 是一个将专家注意力视为核心约束的主动学习框架，它按「所选动作与最终结果之间的互信息」对决策排序，只在最高价值决策上请求专家反馈，并把修正结果蒸馏为可复用规则。
正文
Abstract:LLM agents executing multi-step workflows fail in two fundamentally different ways: recoverable slips, which further attempts can repair, and knowledge gaps, which retrying cannot resolve. Expert feedback can address the latter, but expert attention is scarce. The key challenge is therefore not whether to ask for feedback, but where feedback can change the outcome. Existing approaches either avoid expert feedback, allowing agents to reinforce their own errors, or allocate feedback at the task level without identifying consequential decisions. We present SynAct, an active learning framework that treats expert attention as the binding constraint and allocates feedback at the level of individual decisions. SynAct ranks decisions by the mutual information between the selected action and the terminal outcome across repeated rollouts, queries an expert at the highest-value decisions, and distills the resulting corrections into transferable rules that the agent can reuse without expert access at deployment. We theoretically characterize what this signal identifies under finite repeated sampling: with k rollouts and at least m observations per action, outcome-constant decisions have zero mutual information, while outcome-separating decisions have mutual information at least H2(m/k) bits, yielding exact separation for any threshold t in (0,H2(m/k)). This also explains why success-based acquisition can be blind when all rollouts fail: success is constant even when failed runs reach different outcomes. Across four agent benchmarks and four models spanning three families, SynAct improves held-out accuracy in all 16 evaluated settings, by +11.7pp on average. The same signal transfers to inference-time routing, where SynAct outperforms budget-matched cascade, uncertainty, and expert-judge routers in 15 of 16 settings, while a single targeted correction outperforms seven additional unaimed retries.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.12651 [cs.LG] |
| (or arXiv:2610.12651v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12651 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ning Liu [view email]
[v1]
Thu, 8 Oct 2026 19:20:10 UTC (642 KB)
来源：arXiv：cs.LG · arxiv.org