3 papers
cs.AI2026
Programmatic Context Augmentation for LLM-based Symbolic Regression
Hao Liu, Xiao-Wen Yang, Atharva Sehgal +4
Symbolic regression (SR), the task of discovering mathematical expressions that best describe a given dataset, remains a fundamental challenge in scientific discovery. Traditional…
cs.AI2026
Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards
Shuze Daniel Liu, Claire Chen, Jiabao Sean Xiao +4
The recent advancement of Large Language Models (LLMs) has established their potential as autonomous interactive agents. However, they often struggle in strategic games of incomple…
cs.LG2026
Actor-Curator: Co-adaptive Curriculum Learning via Policy-Improvement Bandits for RL Post-Training
Zhengyao Gu, Jonathan Light, Raul Astudillo +7
Post-training large foundation models with reinforcement learning typically relies on massive and heterogeneous datasets, making effective curriculum learning both critical and cha…