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New research frames LLM post-training as state-distribution shaping

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have proposed a new perspective on large language model post-training, viewing it as a process of shaping the distribution of states rather than solely focusing on tokens. This state-distribution shaping approach was tested using Qwen3-0.6B-Base on GSM8K, TruthfulQA, and MMLU benchmarks. The study found that supervised fine-tuning (SFT) can lead to retention loss if overdone, while on-policy distillation and reinforcement learning can improve performance without sacrificing retention. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT This research offers a new theoretical lens for understanding and potentially improving LLM post-training techniques like SFT, RL, and distillation.

RANK_REASON Academic paper proposing a new theoretical framework for LLM post-training methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · 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 · 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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