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Description

[Please describe the background, purpose, changes made, and how to test this PR]

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Summary of Changes

Hello @hiyuchang, 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 configuration for SFT (Supervised Fine-Tuning) warmup and related training parameters within benchmark templates. It adjusts sampling strategies and loss aggregation modes to align with updated defaults. Furthermore, it enhances documentation by clarifying the release status of a specific SFT dataset and provides clearer, commented-out examples for integrating SFT warmup into existing training pipelines.

Highlights

  • Configuration Updates: The sample_strategy in alfworld-template.yaml and gsm8k-template.yaml has been changed from warmup to default. Additionally, loss_agg_mode in alfworld-template.yaml was updated from seq-mean-token-sum to token-mean.
  • Documentation Clarification: The alfworld.md report was updated to clarify that the SFT dataset mentioned will be released soon.
  • SFT Warmup Examples: Commented-out sections demonstrating how to enable SFT warmup before RFT stages have been added to alfworld.yaml and gsm8k.yaml example configurations, including a fix for the storage_type in gsm8k.yaml.
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Code Review

This pull request correctly updates configuration files to replace the deprecated warmup sample strategy with default. It also introduces commented-out examples for SFT warmup stages in alfworld.yaml and gsm8k.yaml. While these changes are a good step, I've identified a couple of areas for improvement in the example configurations to enhance their clarity and ensure they are complete and functional for users who choose to enable them.

@pan-x-c pan-x-c merged commit 0a72680 into modelscope:main Dec 11, 2025
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