BOTS reference evaluation results collection

[ShenQianli]

Description

As the title says.

• BOTS reference evaluation results collection workflow
• Data path configuration update in scripts
• Doc update

Checklist

Please check the following items before code is ready to be reviewed.

• Code has passed all tests
• Docstrings have been added/updated in Google Style
• Documentation has been updated
• Code is ready for review

[gemini-code-assist]

Summary of Changes

Hello @ShenQianli, 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 enhances the BOTS framework by adding an optional feature for collecting and integrating reference model evaluation results. It provides a structured way to evaluate a specified reference model against a dataset, log the performance metrics, and then merge these metrics back into the original dataset as a new feature. This allows for more comprehensive analysis and utilization of reference model performance within the BOTS ecosystem.

Highlights

• Reference Evaluation Workflow: Introduced a new workflow for collecting reference evaluation results for BOTS, allowing users to evaluate a reference model and integrate its performance metrics.
• Data Path Configuration: Updated bots.yaml and random.yaml to use manually configurable data paths, replacing previous environment variable placeholders for clarity and user control.
• Evaluation Result Integration Script: Added a new Python script (ref_eval_collect.py) designed to integrate collected evaluation results into existing datasets, adding them as a new column.
• Documentation Update: Updated both English and Chinese README.md files with comprehensive instructions on how to set up and run the new reference evaluation process.
• New Workflow Class: Implemented BOTSRefEvalCollectMathBoxedWorkflow to handle the logging of detailed evaluation results (model version, rewards, question, truth) to a JSONL file, ensuring safe concurrent writes with file locking.

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[gemini-code-assist]

Code Review

This pull request introduces a new workflow and script for collecting reference evaluation results and integrating them into existing datasets. It also includes updates to the documentation and data path configurations in bots.yaml and random.yaml. The changes are generally well-structured, but I've identified a critical bug in the new Python script that overrides a command-line argument, a misleading log message, and some maintainability improvements regarding import statements in the new workflow class.