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When Do Larger Batches Help Scale LLM Reinforcement Learning?
Batch size trade-offs in LLM reinforcement learning efficiency
Tencent researchers investigate how batch size affects the efficiency of reinforcement learning training for large language models, analyzing the trade-off between throughput gains and sample efficiency.
The study develops a framework to identify the batch size that minimizes wall-clock time to reach target performance, showing that while moderate batch increases with learning-rate retuning can improve efficiency, excessively large batches incur sample costs that outweigh throughput benefits.
From the source
A larger batch can be either an accelerator or a speed bump. In our experiments, a moderately larger batch with learning-rate retuning preserves learning efficiency while improving throughput. But once the batch becomes too large, the additional sample cost can outweigh the throughput gain.
hunyuan.tencent.com
