Hunyuan Research: Batch-Size Scaling with LR Retuning Boosts PPO Throughput 2.29x
TencentHunyuan · x · 2026-09-24
Tencent Hunyuan's new research extends classical critical-batch-size theory to online LLM RL, where the model generates its own training data and rollout generation and training scale differently.
- Across GRPO and PPO, learning-rate retuning preserves learning per response over a bounded range of batch sizes
- On fixed hardware, scaling batch size improves PPO generation-stage throughput by up to 2.29x
- The best measured GRPO configuration reaches the same validation target in 29% less time
Directly relevant to compute efficiency in large-scale RL training.
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