arXiv 2610.07767 · cs.LG · 2026-10-06
TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models
Xin Wang, Hao Yu, Zhengyang Zhuge, Bochao Mao, Zheng Li, Junda Feng, Yuyan Luo, Yi Zhang 等 12 人
Qwen
在最新榜 #235 本站收录于 2026-10-21
一句话要点
Qwen 团队的 TRACE 对齐训练与 rollout 两条 FP4 量化路径，用于 MoE 大模型的低精度强化学习。
Qwen's TRACE aligns the training and rollout FP4 quantisation paths for low-precision RL of MoE language models.
阅读 arXiv 原文 PDF Hugging Face 讨论
主题与学科
同主题：高效训练与推理
- The Teacher Is a Direction, Not a Destination: Extrapolating RL-Induced Representation Residuals in On-Policy Distillation ▲511
- Scaling Properties of Same-Family On-Policy Distillation ▲327
- Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillation ▲251
- On-Policy or Off-Policy Learning? A Systematic Study of Distillation Dynamics ▲205
- Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation ▲181
数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-22。
要点由本站根据摘要自写，不转载摘要原文，请阅读原文。