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Description

Implemented sPPO. Added example config. Tested example config.

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  • Code has passed all tests
  • Docstrings have been added/updated in Google Style
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Summary of Changes

Hello @yaochaorui, 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 introduces the sPPO (surrogate Proximal Policy Optimization) algorithm, integrating it into the system. It includes the core algorithm implementation, a dedicated policy loss function, and comprehensive example configurations for applying sPPO to the GSM8k dataset, facilitating its immediate use and testing.

Highlights

  • sPPO Algorithm Integration: Implemented the sPPO algorithm, including its registration and default configuration within the trinity.algorithm module.
  • Dedicated sPPO Policy Loss Function: Added sPPOPolicyLossFn to calculate the sPPO loss, which involves clipping the probability ratio based on a specified epsilon value.
  • New sPPO GSM8k Example: Provided a complete example setup for running sPPO on the GSM8k dataset, including a README, main configuration (gsm8k.yaml), and training configuration (train_gsm8k.yaml).
  • Configuration Refinement: Updated an existing configuration file (examples/asymre_gsm8k/gsm8k.yaml) with clearer project naming and relevant research paper references.
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Code Review

This pull request introduces the sPPO algorithm, along with an example configuration for the GSM8k dataset. The implementation of the new algorithm in trinity/algorithm/algorithm.py and the associated example files are well-structured. However, I've found a critical issue in the sPPO loss function implementation which does not seem to match the formulation in the referenced paper. I've also noted a couple of minor issues in the configuration files, such as an incomplete comment and an invalid link. Please see my detailed comments for suggestions.

@yanxi-chen yanxi-chen merged commit 57afe2f into modelscope:main Sep 1, 2025
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2 participants