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TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models

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Briefings

This article presents TRACE, an FP4 quantization framework for RL training of MoE language models. TRACE uses rollout-side quantization outcomes to guide training-side rounding decisions, reducing the discrepancy between training and rollout paths. Evaluated on four large MoE models, TRACE achieves RL performance comparable to BF16 rollout while enabling up to 5.4x rollout speedup and strong final FP4 performance.

Key points

• TRACE is an FP4 quantization framework specifically designed for RL training of Mixture-of-Experts (MoE) language models.
• TRACE uses rollout-side quantization outcomes to guide training-side rounding decisions, directly reducing the discrepancy between training and rollout paths.

Context

Reinforcement learning for post-training LLMs incurs substantial computation and memory overhead during rollout generation, motivating low-precision rollout for efficient RL training. Existing FP4 RL methods optimize quantization accuracy on training and rollout paths independently, whereas TRACE addresses this by aligning them.

Limitations

Evaluation was conducted on four large-scale MoE language models across reasoning, coding, and long-horizon RL tasks. Results show comparable RL performance to BF16 rollout but do not specify the exact hardware or software environment used for the experiments.

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