TRIAGE stabilizes native NVFP4 RL training, hits full-precision quality at 2.3x throughput

InfiX-ai · hf · 2026-10-08

InfiX-ai's new paper tackles instability in low-precision (native NVFP4, W4A4) reinforcement learning for LLMs:

Mismatch between learner and sampler execution destabilizes policy optimization, and mismatch magnitude alone can't identify which updates are harmful.
The authors characterize how mismatch interacts with the policy-gradient direction, distinguishing amplifying vs. contracting updates; native NVFP4 runs show an early imbalance favoring negative-advantage, negative-gap updates whose tail tokens concentrate in a few response segments before spreading globally.
TRIAGE performs segment-level diagnosis to selectively rebalance policy-gradient updates with bounded repair, while keeping W4A4 forward execution on both sampler and learner.
On Qwen3-4B and Qwen3-30B-A3B it trains stably, matches full-precision performance across five math reasoning benchmarks, and delivers up to 2.3x rollout throughput over BF16.

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