[Draft]feat: add NVFP4 QAT (Quantization-Aware Training) support for verl FS…

[zhangyimi]

Description

This PR adds support for NVFP4 Quantization-Aware Training (QAT) with FSDP, enabling W4A16 (weight-only) quantization during RL training.

What's included

verl/utils/qat/ module: QATLinear (Triton FP4 fake quantization), scale fusion, NVFP4 quantizer, and vLLM dynamic weight loading patches
Recipe scripts and configs for Qwen3-30B-A3B W4A16 (full quantization & FFN-only quantization)
Detailed README with implementation overview and experimental results

Key Results

Validated on Qwen3-8B-Base (Dense) and Qwen3-30B-A3B-Base (MoE): W4A16 QAT achieves training accuracy on par with BF16 baseline, while without QAT the KL divergence explodes and training crashes.
70.3% weight memory reduction on Qwen3-30B-A3B during rollout (56.88 GiB → 16.89 GiB), freeing ~40 GiB for additional KV Cache capacity.

verl-recipe PR:verl-project/verl-recipe#36
README: https://github.com/zhangyimi/verl-recipe/blob/dfbf09cd66c66ecbc4b9cea925a6885e5e53f2b1/qat/README.md

What does this PR do?

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