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「微调已死」再添筹码,谷歌扩展AI自我进化范式,成功经验与失败教训双向学习
3 6 Ke·2025-10-13 02:37

Core Insights - The recent discussions around "fine-tuning is dead" have gained significant attention in academia, particularly due to a paper from Stanford University, SambaNova, and UC Berkeley introducing a technique called Agentic Context Engineering, which allows language models to self-improve without fine-tuning [1] - Google previously proposed a similar concept called ReasoningBank, which serves as an innovative memory framework for agent systems, enabling them to extract and organize memory items from their own experiences without requiring true labels [1][3] Summary by Sections ReasoningBank Overview - ReasoningBank captures effective strategies from successes and extracts important lessons from failures, abstracting them into actionable principles [1] - The process operates in a closed loop where agents retrieve relevant memories from ReasoningBank to guide their actions on new tasks, continuously evolving and enhancing their strategic capabilities [1][3] Memory Structure and Integration - ReasoningBank consists of structured memory items designed from past experiences, retaining transferable reasoning patterns and strategies [6] - Each memory item includes a title, a brief description, and content detailing reasoning steps, decision rationale, or operational insights, making them comprehensible for humans and usable for machines [6][7] Testing and Performance - Google has conducted extensive experiments on challenging benchmarks, including web browsing and software engineering tasks, demonstrating that ReasoningBank outperforms baseline methods in both effectiveness (up to 34.2% improvement) and efficiency (16.0% reduction in interaction steps) [9][11] - The integration of ReasoningBank with memory-aware test-time extension (MaTTS) has shown to create a strong synergy, enhancing the agent's ability to learn from both successful and failed trajectories [12][13] Experimental Results - The experiments indicate that both parallel and sequential extensions improve performance, with ReasoningBank achieving higher resolve rates compared to models without memory mechanisms [11][13] - The results highlight the effectiveness of ReasoningBank in various tasks, showcasing its potential as a key component in memory-based experience expansion for agents [12][13]