6 papers
PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Jushan Chen, Santiago Paternain
Recently, diffusion models have gained popularity and attention in trajectory optimization due to their capability of modeling multi-modal probability distributions. However, addre…
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…
ConstrainedSQL: Training LLMs for Text2SQL via Constrained Reinforcement Learning
Weiqin Chen, Nhan Huu Pham, Michael Robert Glass +4
Reinforcement learning (RL) has demonstrated significant promise in enhancing the reasoning capabilities of Text2SQL LLMs, especially with advanced algorithms such as GRPO and DAPO…
Learning Closed-Loop Parametric Nash Equilibria of Multi-Agent Collaborative Field Coverage
Jushan Chen, Santiago Paternain
Multi-agent reinforcement learning is a challenging and active field of research due to the inherent nonstationary property and coupling between agents. A popular approach to model…
DISC: Dynamic Decomposition Improves LLM Inference Scaling
Jonathan Light, Wei Cheng, Benjamin Riviere +6
Inference scaling methods for LLMs often rely on decomposing problems into steps (or groups of tokens), followed by sampling and selecting the best next steps. However, these steps…
Filtering Learning Histories Enhances In-Context Reinforcement Learning
Weiqin Chen, Xinjie Zhang, Dharmashankar Subramanian +1
Transformer models (TMs) have exhibited remarkable in-context reinforcement learning (ICRL) capabilities, allowing them to generalize to and improve in previously unseen environmen…