1 citations · 1 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…