activity
20242026
most citedDiffusing States and Matching Scores: A New Framework for Imitation Learning

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.RO2026

WEAVER, Better, Faster, Longer: An Effective World Model for Robotic Manipulation

Arnav Kumar Jain, Yilin Wu, Jesse Farebrother +2

The potential impacts of world models (WMs, i.e., learned simulators) on robotics are far-reaching -- policy evaluation, policy improvement, and test-time planning -- all with limi…

cs.RO2026

Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering

Hyun Joe Jeong, Gokul Swamy, Andrea Bajcsy

Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically…

cs.LG2026

Back to Blackwell: Closing the Loop on Intransitivity in Multi-Objective Preference Fine-Tuning

Jiahao Zhang, Lujing Zhang, Keltin Grimes +3

A recurring challenge in preference fine-tuning (PFT) is handling (i.e., cyclic) preferences. Intransitive preferences often stem from either …

cs.LG2025

Scaling Offline RL via Efficient and Expressive Shortcut Models

Nicolas Espinosa-Dice, Yiyi Zhang, Yiding Chen +5

Diffusion and flow models have emerged as powerful generative approaches capable of modeling diverse and multimodal behavior. However, applying these models to offline reinforcemen…

cs.LG2025

Efficient Imitation under Misspecification

Nicolas Espinosa-Dice, Sanjiban Choudhury, Wen Sun +1

We consider the problem of imitation learning under misspecification: settings where the learner is fundamentally unable to replicate expert behavior everywhere. This is often true…

cs.LG2024★ 1 cited

Diffusing States and Matching Scores: A New Framework for Imitation Learning

Runzhe Wu, Yiding Chen, Gokul Swamy +2

Adversarial Imitation Learning is traditionally framed as a two-player zero-sum game between a learner and an adversarially chosen cost function, and can therefore be thought of as…

cs.LG2024

Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF

Zhaolin Gao, Wenhao Zhan, Jonathan D. Chang +4

Large Language Models (LLMs) have achieved remarkable success at tasks like summarization that involve a single turn of interaction. However, they can still struggle with multi-tur…