冻结权重下的智能体自我改进：失败轨迹的价值与局限
Agent Self-Improvement with Frozen Weights: The Added Value and Limits of Failure Traces
AI 导读
arXiv 论文评估 AI 智能体的 harness 能否从失败轨迹中学习。研究用固定权重的 90 亿参数开源模型测试，由更大的教师模型写笔记：在生成的客服决策任务上，基于失败的笔记比不接触失败的流程准确率提升 4.0 个百分点，而仅靠尝试并保留更优笔记的流程已达到总提升的约 60%；在完整客服对话中同样的笔记未显示改进。盲审还判定 240 个工具使用测试项中 146 个有缺陷。
正文 · 原文
Abstract:We evaluate whether an AI agent's harness, the notes around its model, can learn from the agent's failure traces beyond a matched search that never sees them. It can, but less than it looks. We tested an open 9-billion-parameter model with fixed weights; a larger teacher model wrote its notes. On generated customer-service decisions, where both note writers were told the target rules, notes based on the agent's mistakes improved accuracy by 4.0 percentage points over notes from the same process with the mistakes withheld. That process alone, trying and keeping better-scoring notes, reached about 60% of the total improvement. In full customer-service conversations the same notes showed no demonstrated improvement. Separately, a strict screen of single lessons admitted two customer-service lessons, still awaiting tests on new data, and one tool-use lesson, written without seeing the agent's mistakes, which showed no demonstrated gain on new tool-use tasks. A neutral note with no procedure scored higher than the learned lessons (exploratory). A blind audit judged 146 of 240 tool-use test items defective.
| Comments: | 6 pages, 1 figure, 1 table. Code and data: this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| ACM classes: | I.2.6; I.2.7; I.2.11 |
| Cite as: | arXiv:2611.11342 [cs.LG] |
| (or arXiv:2611.11342v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.11342 arXiv-issued DOI via DataCite |
Submission history
From: Arkid Mitra [view email]
[v1]
Sun, 8 Nov 2026 12:39:45 UTC (208 KB)
[v2]
Wed, 11 Nov 2026 01:46:48 UTC (208 KB)
来源：arXiv cs.AI · arxiv.org