1 citations · 1 across the 3 of their papers we have counts for
5 papers
Scaling Agent Learning via Experience Synthesis
Zhaorun Chen, Zhuokai Zhao, Kai Zhang +15
While reinforcement learning (RL) can empower autonomous agents by enabling self-improvement through interaction, its practical adoption remains challenging due to costly rollouts,…
SPICE: Self-Play In Corpus Environments Improves Reasoning
Bo Liu, Chuanyang Jin, Seungone Kim +7
Self-improving systems require environmental interaction for continuous adaptation. We introduce SPICE (Self-Play In Corpus Environments), a reinforcement learning framework where…
The Era of Real-World Human Interaction: RL from User Conversations
Chuanyang Jin, Jing Xu, Bo Liu +6
We posit that to achieve continual model improvement and multifaceted alignment, future models must learn from natural human interaction. Current conversational models are aligned…
LLaVA-Critic-R1: Your Critic Model is Secretly a Strong Policy Model
Xiyao Wang, Chunyuan Li, Jianwei Yang +4
In vision-language modeling, critic models are typically trained to evaluate outputs -- assigning scalar scores or pairwise preferences -- rather than to generate responses. This s…
TextArena
Leon Guertler, Bobby Cheng, Simon Yu +3
TextArena is an open-source collection of competitive text-based games for training and evaluation of agentic behavior in Large Language Models (LLMs). It spans 57+ unique environm…