257 citations · 1k across the 96 of their papers we have counts for
16 papers · 1 filter
Post-Training, Finetuning, and Steering:A Field Guide for Curating Training Data
Maria Attarian, Yifan Ruan, Claas Voelcker +5
While Vision-Language-Action models have enabled impressive cross-task transfer in zero-shot settings, \emph{adaptation} methods are often necessary to achieve consistent performan…
Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL
Dongsu Lee, Sooraj Sathish, Joonkyung Kim +2
We argue that cooperative multi-agent reinforcement learning (MARL) should treat test-time coordination as part of the problem definition rather than a deployment artifact. Our foc…
Test-time Multi-agent Coordination by Decomposed Value Gradient Flow
Dongsu Lee, Haoran Xu, Amy Zhang
Offline multi-agent reinforcement learning (MARL) faces a persistent trade-off. Expressive generative policies can represent multi-modal coordination in the data, but cannot distin…
Foresight: Iterative Reasoning About Clues that Matter for Navigation
Arthur Zhang, Carl Qi, Donne Su +3
Open-world mapless navigation from sparse language instructions requires resolving underspecified goals and inferring which environmental cues are relevant for reaching the goal. F…
Trajectory-Refined Distillation
Li Jiang, Haoran Xu, Yichuan Ding +1
On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providing dense per-token teacher supervision along the student's own rollout…
Exploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL
Sarthak Dayal, Abhinav Peri, Carl Qi +4
Hierarchical Reinforcement Learning (HRL) promises to solve long-horizon Reinforcement Learning (RL) tasks more efficiently than non-hierarchical counterparts by discovering and re…
Reinforcement Learning via Value Gradient Flow
Haoran Xu, Kaiwen Hu, Somayeh Sojoudi +1
We study behavior-regularized reinforcement learning (RL), where regularization toward a reference distribution (the dataset in offline RL or the base model in LLM RL finetuning) i…
Filtered Reasoning Score: Evaluating Reasoning Quality on a Model's Most-Confident Traces
Manas Pathak, Xingyao Chen, Shuozhe Li +2
Should we trust Large Language Models (LLMs) with high accuracy? LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reason…
The PokeAgent Challenge: Competitive and Long-Context Learning at Scale
Seth Karten, Jake Grigsby, Tersoo Upaa +28
We present the PokeAgent Challenge, a large-scale benchmark for decision-making research built on Pokemon's multi-agent battle system and expansive role-playing game (RPG) environm…
A Recipe for Stable Offline Multi-agent Reinforcement Learning
Dongsu Lee, Daehee Lee, Amy Zhang
Despite remarkable achievements in single-agent offline reinforcement learning (RL), multi-agent RL (MARL) has struggled to adopt this paradigm, largely persisting with on-policy t…
Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal +3
Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task. These methods, however, are only able to produce near-optimal policie…
Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning
Carl Qi, Xiaojie Wang, Silong Yong +6
Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning as a closed-set classification problem or…
Hierarchical Entity-centric Reinforcement Learning with Factored Subgoal Diffusion
Dan Haramati, Carl Qi, Tal Daniel +3
We propose a hierarchical entity-centric framework for offline Goal-Conditioned Reinforcement Learning (GCRL) that combines subgoal decomposition with factored structure to solve l…
Factored Latent Action World Models
Zizhao Wang, Chang Shi, Jiaheng Hu +4
Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions provide a natural interface for u…
Learning Robust Reasoning through Guided Adversarial Self-Play
Shuozhe Li, Vaishnav Tadiparthi, Kwonjoon Lee +6
Reinforcement learning from verifiable rewards (RLVR) produces strong reasoning models, yet they can fail catastrophically when the conditioning context is fallible (e.g., corrupte…
CARE-RFT: Confidence-Anchored Reinforcement Finetuning for Reliable Reasoning in Large Language Models
Shuozhe Li, Jincheng Cao, Bodun Hu +3
Reinforcement finetuning (RFT) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models. However, we identify a critical trade-off: while un…