activity
20242026
most citedGetting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment

1 citations · 1 across the 8 of their papers we have counts for

collaborators
Showing stat.MLShow all

1 paper · 1 filter

stat.ML2025

Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality

Ruijia Zhang, Siliang Zeng, Chenliang Li +2

The goal of the Inverse reinforcement learning (IRL) task is to identify the underlying reward function and the corresponding optimal policy from a set of expert demonstrations. Wh…