3 papers
cs.LG2026
Statistical Guarantees for Offline Domain Randomization
Arnaud Fickinger, Abderrahim Bendahi, Stuart Russell
Reinforcement-learning (RL) agents often struggle when deployed from simulation to the real-world. A dominant strategy for reducing the sim-to-real gap is domain randomization (DR)…
cs.LG2025
Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts
Jiahai Feng, Stuart Russell, Jacob Steinhardt
Pretrained language models (LMs) can generalize to implications of facts that they are finetuned on. For example, if finetuned on ``John Doe lives in Tokyo," LMs can correctly answ…
cs.CL2024
Monitoring Latent World States in Language Models with Propositional Probes
Jiahai Feng, Stuart Russell, Jacob Steinhardt
Language models are susceptible to bias, sycophancy, backdoors, and other tendencies that lead to unfaithful responses to the input context. Interpreting internal states of languag…