Polaris：通过经验抽象策略修复实现小型语言模型的Gödel代理框架
Polaris: A Gödel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair
AI总结：
Polaris通过经验抽象策略修复实现小型语言模型的Gödel代理框架，通过分析、策略形成、抽象和最小代码修补实现策略更新，提升模型在多个基准测试中的表现。
摘要：
Gödel代理实现递归自我改进：一个代理会检查自己的策略并追踪，然后在测试循环中修改该策略。我们引入Polaris，一种针对紧凑模型的Gödel代理，通过经验抽象进行策略修复，将失败转化为策略更新，通过分析、策略形成、抽象和最小代码修补的结构循环实现。与响应层面自我纠正或参数调优不同，Polaris通过小、可审计的修补进行策略层面的修改，这些修补会持续存在于策略中，并在每个基准测试中重新应用于未见过的实例。作为循环的一部分，代理进行元推理：它解释其错误，提出对自身策略的具体修订，然后更新策略。为了实现累积策略细化，我们引入经验抽象，将失败提炼成紧凑、可重用的策略，这些策略能转移到未见过的实例。在MGSM、DROP、GPQA和LitBench（涵盖算术推理、组合推理、研究生级问题解决和创意写作评估）上，一个配备Polaris的70亿参数模型在基础策略和竞争基线中取得了一致提升。
英文摘要：
Gödel agent realize recursive self-improvement: an agent inspects its own policy and traces and then modifies that policy in a tested loop. We introduce Polaris, Gödel agent for compact models that performs policy repair via experience abstraction, turning failures into policy updates through a structured cycle of analysis, strategy formation, abstraction, and minimal code patch repair with conservative checks. Unlike response level self correction or parameter tuning, Polaris makes policy level changes with small, auditable patches that persist in the policy and are reused on unseen instances within each benchmark. As part of the loop, the agent engages in meta reasoning: it explains its errors, proposes concrete revisions to its own policy, and then updates the policy. To enable cumulative policy refinement, we introduce experience abstraction, which distills failures into compact, reusable strategies that transfer to unseen instances. On MGSM, DROP, GPQA, and LitBench (covering arithmetic reasoning, compositional inference, graduate-level problem solving, and creative writing evaluation), a 7-billion-parameter model equipped with Polaris achieves consistent gains over the base policy and competitive baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
