arXiv cs.AI· Lirui Luo, Kelong Mao, Xinwei Yang, Rongqing Li, Luyu Chen, Kieran Wong, Yudong Guo, Xinrui Wang, Jiayin Zhu, Gu Simiu, Sulong Xu, Cong Fang·· 3 小时前AI 评分46
CAMP-II 用策略内蒸馏释放预训练文件操作的记忆潜能，处理长程任务
Coding Agent Memory Post-Training II: Unlocking the Memory Potential of Pre-trained File Operations for Long-Horizon Tasks via On-Policy Distillation
AI 导读
研究者提出 CAMP-II，通过全异步策略内蒸馏（OPD），让单个策略直接从冻结教师学习基于文件的记忆行为，解决教师与学生都不熟悉记忆工具导致的蒸馏失效问题。配套的 CAMG-II 环境套件覆盖 Shop、Coding、AutoResearch、General、Visual 五类长程智能体 RL 任务，提供 Bash 访问和持久工作区以便跨上下文替换创建、修改、搜索和复用文件。
正文 · 原文
Authors:Lirui Luo, Kelong Mao, Xinwei Yang, Rongqing Li, Luyu Chen, Kieran Wong, Yudong Guo, Xinrui Wang, Jiayin Zhu, Gu Simiu, Sulong Xu, Cong Fang
Abstract:Language-model agents increasingly tackle long-horizon tasks whose interaction histories exceed the model's active context. They must therefore decide what to write down and what to read back, and when to do either. Recent work has begun to use on-policy distillation (OPD) to make memory control part of the policy, relying on memory tools and compaction protocols that lie outside either model's pre-training. However, this weakens the distillation on both sides: the teacher cannot give useful guidance on an interface it has never used, and the student samples too few meaningful memory actions for that guidance to take effect. To study this problem, we introduce Coding Agent Memory Gym II (CAMG-II), a suite of long-horizon agentic-RL environments spanning Shop, Coding, AutoResearch, General, and Visual. Alongside each environment's native task interface, CAMG-II provides ordinary Bash access and an episode-persistent workspace, enabling the policy to create, revise, search, and reuse files as memory across context replacements. We also introduce Coding Agent Memory Post-Training II (CAMP-II), which trains a single policy jointly across all five environments with fully asynchronous OPD to learn this file-based memory behavior directly from a frozen teacher with a prior for file operations. On CAMG-II, CAMP-II obtains 2.97 times the gain of the strongest OPD baseline. The memory behavior CAMP-II learns generalizes to two external benchmarks, Terminal-Bench 2.1 and SWE-Bench Pro V2, with improved success rates.
| Comments: | 40 pages, 6 figures, 8 tables |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.14009 [cs.LG] |
| (or arXiv:2611.14009v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14009 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lirui Luo [view email]
[v1]
Tue, 10 Nov 2026 12:49:24 UTC (11,663 KB)
来源：arXiv cs.AI · arxiv.org