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The paper identifies instability in reinforcement learning for LLMs caused by discrepancies between training and inference, due to architectural differences and precision gaps. It proposes Adaptive Control of Training-Inference Discrepancy (ACRL) to stabilize RL training by mitigating these factors.
The paper identifies instability in reinforcement learning for LLMs caused by discrepancies between training and inference, due to architectural differences and precision gaps. It proposes Adaptive Control of Training-Inference Discrepancy (ACRL) to stabilize RL training by mitigating these factors.
AI-assisted summary based on the listed source.
Reinforcement Learning (RL) training for Large Language Models (LLMs) often suffers from instability due to the discrepancy between training and inference. This training-inference discrepancy stems from two primary factors: an architectural separation between training and inference engines, and the use of...
Reducing training-inference discrepancies can improve the stability and effectiveness of reinforcement learning in LLMs, potentially leading to more reliable model performance. Addressing precision and architectural gaps is crucial for deploying LLMs in real-world inference scenarios.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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