5 papers
Generating Data-Driven Reasoning Rubrics for Domain-Adaptive Reward Modeling
Kate Sanders, Nathaniel Weir, Sapana Chaudhary +2
An impediment to using Large Language Models (LLMs) for reasoning output verification is that LLMs struggle to reliably identify errors in thinking traces, particularly in long out…
MaxCode: A Max-Reward Reinforcement Learning Framework for Automated Code Optimization
Jiefu Ou, Sapana Chaudhary, Kaj Bostrom +4
Large Language Models (LLMs) demonstrate strong capabilities in general coding tasks but encounter two key challenges when optimizing code: (i) the complexity of writing optimized…
VeriCoT: Neuro-symbolic Chain-of-Thought Validation via Logical Consistency Checks
Yu Feng, Nathaniel Weir, Kaj Bostrom +5
LLMs can perform multi-step reasoning through Chain-of-Thought (CoT), but they cannot reliably verify their own logic. Even when they reach correct answers, the underlying reasonin…
Offline Learning and Forgetting for Reasoning with Large Language Models
Tianwei Ni, Allen Nie, Sapana Chaudhary +3
Leveraging inference-time search in large language models has proven effective in further enhancing a trained model's capability to solve complex mathematical and reasoning problem…
AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents
Ke Yang, Yao Liu, Sapana Chaudhary +4
Autonomy via agents using large language models (LLMs) for personalized, standardized tasks boosts human efficiency. Automating web tasks (like booking hotels within a budget) is i…