1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
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…
cs.RO2025★ 1 cited
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…
cs.LG2025
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…