565 citations · 588 across the 12 of their papers we have counts for
5 papers · 1 filter
AgentPersonaBench: Benchmarking Persona-Driven User Simulation
Jintao Huang, Yifan Wang, Hongyu Shen +43
We introduce AgentPersonaBench (APB), a benchmark evaluating whether persona conditioning faithfully steers downstream agent behavior. While language models are increasingly deploy…
Discovering High-Quality Chess Puzzles with Offline Reinforcement Learning
Allen Nie, Anirudhan Badrinath, Nicholas Tomlin +5
Learning and skill mastery require extensive and deliberate practice. In many learning settings, producing high-quality pedagogical materials can require a high level of domain exp…
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…
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…