12 citations · 21 across the 18 of their papers we have counts for
8 papers · 1 filter
On Safety Risks in Experience-Driven Self-Evolving Agents
Weixiang Zhao, Yichen Zhang, Yingshuo Wang +8
Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduc…
Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewards
Jeff Da, Clinton Wang, Xiang Deng +3
Reinforcement Learning from Verifiable Rewards (RLVR) has been widely adopted as the de facto method for enhancing the reasoning capabilities of large language models and has demon…
ToolComp: A Multi-Tool Reasoning & Process Supervision Benchmark
Vaskar Nath, Pranav Raja, Claire Yoon +1
Despite recent advances in AI, the development of systems capable of executing complex, multi-step reasoning tasks involving multiple tools remains a significant challenge. Current…
Revisiting the Superficial Alignment Hypothesis
Mohit Raghavendra, Vaskar Nath, Sean Hendryx
The Superficial Alignment Hypothesis posits that almost all of a language model's abilities and knowledge are learned during pre-training, while post-training is about giving a mod…
Pre-Training Multimodal Hallucination Detectors with Corrupted Grounding Data
Spencer Whitehead, Jacob Phillips, Sean Hendryx
Multimodal language models can exhibit hallucinations in their outputs, which limits their reliability. The ability to automatically detect these errors is important for mitigating…
Learning Goal-Conditioned Representations for Language Reward Models
Vaskar Nath, Dylan Slack, Jeff Da +4
Techniques that learn improved representations via offline data or self-supervised objectives have shown impressive results in traditional reinforcement learning (RL). Nevertheless…
A Careful Examination of Large Language Model Performance on Grade School Arithmetic
Hugh Zhang, Jeff Da, Dean Lee +12
Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning. However, there is growing concern that some of this performance actuall…
A Baseline Analysis of Reward Models' Ability To Accurately Analyze Foundation Models Under Distribution Shift
Will LeVine, Benjamin Pikus, Anthony Chen +1
Foundation models, specifically Large Language Models (LLMs), have lately gained wide-spread attention and adoption. Reinforcement Learning with Human Feedback (RLHF) involves trai…