VQV Signal
The paper studies the training-inference mismatch in reinforcement learning with verifiable rewards (RLVR) for large language models, where inference and training engines assign different token probabilities. To correct this, the authors introduce calibrated importance sampling (CIS) to adjust poli...
The paper studies the training-inference mismatch in reinforcement learning with verifiable rewards (RLVR) for large language models, where inference and training engines assign different token probabilities. To correct this, the authors introduce calibrated importance sampling (CIS) to adjust poli...
AI-assisted summary based on the listed source.
We study training-inference mismatch in reinforcement learning with verifiable rewards (RLVR) for large language models, where rollouts are sampled by an inference engine while gradients are computed by a training engine, and the two engines assign different probabilities to the same tokens. To account for this...
Training-inference mismatch can degrade the performance of reinforcement learning in LLMs by causing inconsistent policy updates. CIS offers a method to align training and inference probabilities, potentially improving model reliability and effectiveness.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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