1 citations · 1 across the 8 of their papers we have counts for
9 papers
SeeQ: Training Generalist Value Functions for Long-Horizon Robotic Manipulation
Saksham Singh, Zheyuan Hu, Max Sobol Mark +3
Despite rapid progress, generalist robot policies remain brittle on complex, long-horizon tasks that comprise multiple stages or require repeated attempts and deliberation on the s…
Addressing Over-Refusal in LLMs with Competing Rewards
Taeyoun Kim, Aviral Kumar
Safety training on language models often induces over-refusal: improved safety on harmful prompts at the cost of increased refusal on harmless ones. Though this trade-off can be mi…
BPP: Long-Context Robot Imitation Learning by Focusing on Key History Frames
Max Sobol Mark, Jacky Liang, Maria Attarian +4
Many robot tasks require attending to the history of past observations. For example, finding an item in a room requires remembering which places have already been searched. However…
InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning
Matthew Y. R. Yang, Hao Bai, Ian Wu +3
Outcome-reward reinforcement learning (RL) has proven effective at improving the reasoning capabilities of large language models (LLMs). However, standard RL assigns credit only at…
TRIM: Hybrid Inference via Targeted Stepwise Routing in Multi-Step Reasoning Tasks
Vansh Kapoor, Aman Gupta, Hao Chen +3
Multi-step reasoning tasks like mathematical problem solving are vulnerable to cascading failures, where a single incorrect step leads to complete solution breakdown. Current LLM r…
WebGym: Scaling Training Environments for Visual Web Agents with Realistic Tasks
Hao Bai, Alexey Taymanov, Tong Zhang +2
We present WebGym, the largest-to-date open-source environment for training realistic visual web agents. Real websites are non-stationary and diverse, making artificial or small-sc…
RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction
Zheyuan Hu, Robyn Wu, Naveen Enock +4
Modern paradigms for robot imitation train expressive policy architectures on large amounts of human demonstration data. Yet performance on contact-rich, deformable-object, and lon…
Reasoning as an Adaptive Defense for Safety
Taeyoun Kim, Fahim Tajwar, Aditi Raghunathan +1
Reasoning methods that adaptively allocate test-time compute have advanced LLM performance on easy to verify domains such as math and code. In this work, we study how to utilize th…
Grounded Reinforcement Learning for Visual Reasoning
Gabriel Sarch, Snigdha Saha, Naitik Khandelwal +4
While reinforcement learning (RL) over chains of thought has significantly advanced language models in tasks such as mathematics and coding, visual reasoning introduces added compl…