4 citations · 5 across the 8 of their papers we have counts for
4 papers · 1 filter
Frontier Learning: Training LLM Reasoners at the Edge of Capability
Robin Faro, Shyam Sundhar Ramesh, Ilija Bogunovic +1
Reinforcement Learning-based post-training of Large Language Models (LLM) has been successfully applied to improve their reasoning capabilities. Existing pipelines primarily finetu…
Robust Multi-Objective Controlled Decoding of Large Language Models
Seongho Son, William Bankes, Sangwoong Yoon +3
We introduce Robust Multi-Objective Decoding (RMOD), a novel inference-time algorithm that robustly aligns Large Language Models (LLMs) to multiple human objectives (e.g., instruct…
On Almost Surely Safe Alignment of Large Language Models at Inference-Time
Xiaotong Ji, Shyam Sundhar Ramesh, Matthieu Zimmer +3
We introduce a novel inference-time alignment approach for LLMs that aims to generate safe responses almost surely, i.e., with probability approaching one. Our approach models the…
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces
Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu +2
Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world d…