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Reinforcement learning of large language models is sensitive to training-inference mismatch (TIM), causing instability due to persistent bias drift. The paper identifies drift as the main cause of instability and proposes score centering to stabilize RL under TIM.
Reinforcement learning of large language models is sensitive to training-inference mismatch (TIM), causing instability due to persistent bias drift. The paper identifies drift as the main cause of instability and proposes score centering to stabilize RL under TIM.
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Reinforcement learning (RL) of large language models is notoriously sensitive to small differences between training and inference engines, often referred to as the training-inference mismatch (TIM). However, completely eliminating TIM is impractical, as it would come at a major cost to rollout efficiency. In this...
Understanding and mitigating TIM is crucial for efficient and stable reinforcement learning in large language models. This approach could improve rollout efficiency without fully eliminating TIM, which is impractical.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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