RL-finetuning LLMs from on- and off-policy data with a single algorithm

Name: RL-finetuning LLMs from on- and off-policy data with a single algorithm
Uploaded: 2026-10-09T02:22:15.680688+00:00
Duration: 300 s
Description: The Any-Generation Reward Optimization (AGRO) algorithm uses a single reinforcement learning framework to fine-tune large language models using both on-policy and off-policy data. It leverages 'generation consistency' to derive optimal policies through sample-based learning with theoretical guarantees, showing improved performance on mathematical reasoning tasks over existing methods.

The Any-Generation Reward Optimization (AGRO) algorithm uses a single reinforcement learning framework to fine-tune large language models using both on-policy and off-policy data. It leverages 'generation consistency' to derive optimal policies through sample-based learning with theoretical guarantees, showing improved performance on mathematical reasoning tasks over existing methods.

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