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When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains

Intermediate

Ahmadreza Jeddi, Kimia Shaban et al.Mar 1arXiv

This paper asks a simple question: does reinforcement learning (RL) truly make medical vision-language models (VLMs) smarter, or just help them pick better from answers they already know?

#medical vision-language models#reinforcement learning#supervised fine-tuning

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

RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents

Intermediate

Jialiang Zhu, Gongrui Zhang et al.Feb 2arXiv

Re-TRAC is a new way for AI search agents to learn from each try, write a clean summary of what happened, and then use that summary to do better on the next try.

#Re-TRAC#trajectory compression#deep research agents

Papers3

When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains

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

RE-TRAC: REcursive TRAjectory Compression for Deep Search Agents