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Weak-Driven Learning: How Weak Agents make Strong Agents Stronger

Intermediate

Zehao Chen, Gongxun Li et al.Feb 9arXiv

Big language models can get stuck after fine-tuning because they become too sure of themselves, so normal training stops helping.

#weak-driven learning#logit mixing#weak agents

On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models

Intermediate

Shumin Wang, Yuexiang Xie et al.Feb 3arXiv

The paper builds a simple, math-light rule to predict whether training makes a language model more open-minded (higher entropy) or more sure of itself (lower entropy).

#reinforcement fine-tuning#entropy dynamics#GRPO

Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies

Beginner

Yuqiao Tan, Minzheng Wang et al.Dec 22arXiv

Large language models (LLMs) don’t act as a single brain; inside, each layer and module quietly makes its own mini-decisions called internal policies.

#Bottom-up Policy Optimization#internal layer policy#internal modular policy

Papers3

Weak-Driven Learning: How Weak Agents make Strong Agents Stronger

On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models

Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies