1 citations · 1 across the 2 of their papers we have counts for
6 papers
Dataset Reset Policy Optimization for RLHF
Jonathan D. Chang, Wenhao Zhan, Owen Oertell +4
Reinforcement Learning (RL) from Human Preference-based feedback is a popular paradigm for fine-tuning generative models, which has produced impressive models such as GPT-4 and Cla…
Provable Interactive Learning with Hindsight Instruction Feedback
Dipendra Misra, Aldo Pacchiano, Robert E. Schapire
We study interactive learning in a setting where the agent has to generate a response (e.g., an action or trajectory) given a context and an instruction. In contrast, to typical ap…
Aligning LLM Agents by Learning Latent Preference from User Edits
Ge Gao, Alexey Taymanov, Eduardo Salinas +2
We study interactive learning of LLM-based language agents based on user edits made to the agent's output. In a typical setting such as writing assistants, the user interacts with…
Policy Improvement using Language Feedback Models
Victor Zhong, Dipendra Misra, Xingdi Yuan +1
We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instructio…
The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
Pratyusha Sharma, Jordan T. Ash, Dipendra Misra
Transformer-based Large Language Models (LLMs) have become a fixture in modern machine learning. Correspondingly, significant resources are allocated towards research that aims to…
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…