1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Abstractive Red-Teaming of Language Model Character
Nate Rahn, Allison Qi, Avery Griffin +3
We want language model assistants to conform to a character specification, which asserts how the model should act across diverse user interactions. While models typically follow th…
cs.CL2024★ 1 cited
Controlling Large Language Model Agents with Entropic Activation Steering
Nate Rahn, Pierluca D'Oro, Marc G. Bellemare
The rise of large language models (LLMs) has prompted increasing interest in their use as in-context learning agents. At the core of agentic behavior is the capacity for exploratio…
cs.LG2023
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
Nate Rahn, Pierluca D'Oro, Harley Wiltzer +2
Deep reinforcement learning agents for continuous control are known to exhibit significant instability in their performance over time. In this work, we provide a fresh perspective…