Bridging the Execution Gap in Native NVFP4 Reinforcement Learning

A new diagnostic tool called miniTE targets the instability that arises when LLM reinforcement learning uses separate training and sampling engines with NVFP4 precision.

3 November 2026 · 1 min read · 1 source

A new arXiv paper introduces miniTE, a diagnostic approach for reinforcement learning (RL) with large language models (LLMs) using native NVFP4, a 4-bit floating-point format. The authors note that while NVFP4 promises to accelerate RL, achieving stable learning is hard when training and sampling run on separate engines.

The core problem, they argue, is an 'execution gap' between these engines. The paper shows that matching model weights between the training and sampling sides helps, though the abstract is truncated before full details are given. The work appears to be an early step toward making low-precision RL more reliable in practice.

Source

01miniTE: Diagnosing and Bridging the Execution Gap in Native NVFP4 Reinforcement LearningarXiv

Bridging the Execution Gap in Native NVFP4 Reinforcement Learning

Source

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