Theme · 6 episode(s)

RL for Reasoning

Definition

Reinforcement learning for reasoning trains models to produce useful chains of thought by rewarding correct final answers (or verified intermediate steps) and letting the model figure out the reasoning that gets there. Most of the 2024–2026 jump in math and code performance has roots here.

Episodes covering this

079
An Old Idea From Cognitive Psychology Reshapes How We Reward Reasoning Models
Chen, Xu, Zhao et al. · Tongji University / Shanghai AI Laboratory / Nanyang Technological University·29 min·May 25, 2026
073
When Three LLMs Talk to Each Other, Their Ideas Quietly Stop Moving
Kong, Lai, Piao et al. · University of Toronto·28 min·May 23, 2026
048
How a 30B Open Model Reached Olympiad Gold With the Right Recipe
Li, Zhan, Zhang et al. · Shanghai AI Laboratory / The Chinese University of Hong Kong·31 min·May 16, 2026
041
When the Iteration Teaches the Model to Skip the Iteration
Fein-Ashley, Rashidinejad · University of Southern California·30 min·May 13, 2026
026
What RL Actually Does to Language Models, at the Token Level
Akgül, Kannan, Neiswanger et al. · University of Southern California·24 min·May 08, 2026
009
How Two Silent Library Bugs Quietly Invalidated a Wave of Reasoning Papers
Limozin, Durech, Hoefler et al. · ETH AI Center·23 min·May 02, 2026

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