Tencent RL study: larger batches speed up LLM RL if throughput gains beat sample-efficiency loss
CurieuxExplorer · x · 2026-09-25
Tencent's RL team examined how batch size scaling affects LLM reinforcement learning efficiency.
- Larger batches cut time-to-target performance, provided hardware throughput gains outweigh sample-efficiency losses.
- Using a square-root learning rate rule, per-sample learning progress stays stable across a range of batch sizes.
- On fixed hardware, bigger batches are a practical way to lower RL costs and iterate faster.
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