Post-Training is About States, Not Tokens: A State Distribution View of SFT, RL, and On-Policy Distillation

Documenti analoghi

• GAC: Noise-Aware Adaptive Mixing for Hybrid SFT-RL Post-Training
  di: Hu, Yuelin, et al.
  Pubblicazione: (2026)
• Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead
  di: Kang, Feiyang, et al.
  Pubblicazione: (2025)
• Explainable RL Policies by Distilling to Locally-Specialized Linear Policies with Voronoi State Partitioning
  di: Deproost, Senne, et al.
  Pubblicazione: (2025)
• SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning
  di: Limozin, Alexis, et al.
  Pubblicazione: (2026)
• Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation
  di: Yang, Zhuolin, et al.
  Pubblicazione: (2026)