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New research frames LLM post-training around state distributions, not just tokens

By PulseAugur Editorial · [2 sources] ·

Researchers have proposed a new perspective on large language model post-training, focusing on the distribution of states rather than just tokens. Their study suggests that the source and locality of training states can be as crucial as the supervision signal itself. Experiments using Qwen3-0.6B-Base demonstrated that on-policy distillation from a weaker teacher model could still improve performance across multiple benchmarks, and lightweight reinforcement learning enhanced a specific task while preserving retention. AI

IMPACT This research offers a new lens for understanding and improving LLM post-training, potentially leading to more efficient and effective fine-tuning techniques.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework and experimental results for LLM post-training methods.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Dong Nie ·

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

    arXiv:2605.22731v1 Announce Type: new Abstract: Large language model post-training methods such as supervised fine-tuning (SFT), reinforcement learning (RL), and distillation are often analyzed through their loss functions: maximum likelihood, policy gradients, forward KL, revers…

  2. arXiv cs.AI TIER_1 English(EN) · Dong Nie ·

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

    Large language model post-training methods such as supervised fine-tuning (SFT), reinforcement learning (RL), and distillation are often analyzed through their loss functions: maximum likelihood, policy gradients, forward KL, reverse KL, or related objective-level variants. We st…

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