Keep SQL Experience Buffer behavior consistent with previous versions#248
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hiyuchang merged 10 commits intoagentscope-ai:mainfrom Sep 4, 2025
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Summary of Changes
Hello @pan-x-c, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request refines the data handling and state management within the system to improve the robustness and flexibility of training processes. Key changes include updating the SQL experience buffer's read logic to maintain consistent behavior for certain data types and enabling seamless continuous training for SFT and DPO models. Additionally, the core state management component has been renamed for clarity and its functionality extended to better support persistent training states.
Highlights
- SQL Experience Buffer Behavior: The SQL Experience Buffer now sorts experiences based on their consumption count, prioritizing less consumed experiences. This ensures consistency with previous versions' behavior for 'experience' schema types, while SFT and DPO schema types will use a FIFO (First-In, First-Out) approach.
- State Management Renaming: The
CacheManagerclass and its associated file have been renamed toStateManagerto more accurately reflect its role in managing the running state of the Explorer and Trainer components. - Continuous Training Support: Enhanced support for continuing training for SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimization) models has been implemented. The
StateManagernow tracks and persists the latest task and experience indices, allowing training to resume from a specific point.
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Code Review
This pull request introduces several enhancements, including making the SQL Experience Buffer behavior consistent with previous versions by sorting experiences based on consumption count, renaming CacheManager to StateManager for clarity, and adding support for continuing SFT and DPO training. The changes are well-structured. I've found a potential bug in how the trainer state is saved, which could affect resuming training, and a minor code duplication issue in the tests. My detailed feedback is in the comments below.
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hiyuchang
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Sep 4, 2025
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