8 papers
Formalizing Learning from Language Feedback with Provable Guarantees
Wanqiao Xu, Allen Nie, Ruijie Zheng +3
Interactively learning from observation and language feedback is an increasingly studied area driven by the emergence of large language model (LLM) agents. Despite impressive empir…
Reasoning about Reasoning: BAPO Bounds on Chain-of-Thought Token Complexity in LLMs
Kiran Tomlinson, Tobias Schnabel, Adith Swaminathan +1
Inference-time scaling via chain-of-thought (CoT) reasoning is a major driver of state-of-the-art LLM performance, but it comes with substantial latency and compute costs. We addre…
Understanding the Challenges in Iterative Generative Optimization with LLMs
Allen Nie, Xavier Daull, Zhiyi Kuang +10
Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach…
The Context Gathering Decision Process: A POMDP Framework for Agentic Search
Chinmaya Kausik, Adith Swaminathan, Nathan Kallus
Large Language Model (LLM) agents are deployed in complex environments -- such as massive codebases, enterprise databases, and conversational histories -- where the relevant state…
Lost in Transmission: When and Why LLMs Fail to Reason Globally
Tobias Schnabel, Kiran Tomlinson, Adith Swaminathan +1
Despite their many successes, transformer-based large language models (LLMs) continue to struggle with tasks that require complex reasoning over large parts of their input. We argu…
A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs
Trenton Chang, Tobias Schnabel, Adith Swaminathan +1
Despite advances in large language models (LLMs) on reasoning and instruction-following tasks, it is unclear whether they can reliably produce outputs aligned with a variety of use…