2 citations · 4 across the 10 of their papers we have counts for
10 papers
RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design
Meghana Kshirsagar, Allen Nie, Ching-An Cheng +5
We introduce RosettaSearch, an inference-time multi-objective optimization approach for backbone conditioned protein sequence design. We use large language models (LLMs) as a gener…
POLCA: Stochastic Generative Optimization with LLM
Xuanfei Ren, Allen Nie, Tengyang Xie +1
Optimizing complex systems, ranging from LLM prompts to multi-turn agents, traditionally requires labor-intensive manual iteration. We formalize this challenge as a stochastic gene…
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…
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…
How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
Ying Fan, Jingling Li, Adith Swaminathan +2
We present a novel method, Contextual goal-Oriented Data Augmentation (CODA), which uses commonly available unlabeled trajectories and context-goal pairs to solve Contextual Goal-O…
The Importance of Directional Feedback for LLM-based Optimizers
Allen Nie, Ching-An Cheng, Andrey Kolobov +1
We study the potential of using large language models (LLMs) as an interactive optimizer for solving maximization problems in a text space using natural language and numerical feed…
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
Ching-An Cheng, Allen Nie, Adith Swaminathan
We study a class of optimization problems motivated by automating the design and update of AI systems like coding assistants, robots, and copilots. AutoDiff frameworks, like PyTorc…
PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
Ruijie Zheng, Ching-An Cheng, Hal Daumé +2
Temporal action abstractions, along with belief state representations, are a powerful knowledge sharing mechanism for sequential decision making. In this work, we propose a novel v…
LLF-Bench: Benchmark for Interactive Learning from Language Feedback
Ching-An Cheng, Andrey Kolobov, Dipendra Misra +2
We introduce a new benchmark, LLF-Bench (Learning from Language Feedback Benchmark; pronounced as "elf-bench"), to evaluate the ability of AI agents to interactively learn from nat…
Interactive Robot Learning from Verbal Correction
Huihan Liu, Alice Chen, Yuke Zhu +3
The ability to learn and refine behavior after deployment has become ever more important for robots as we design them to operate in unstructured environments like households. In th…