11 citations · 24 across the 41 of their papers we have counts for
3 papers · 1 filter
Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning
Ömer Faruk Akgül, Rajgopal Kannan, Willie Neiswanger +1
Reinforcement learning has become the standard for improving reasoning in large language models, yet evidence increasingly suggests that RL does not teach new strategies; it redist…
LYNX: Learning Dynamic Exits for Confidence-Controlled Reasoning
Ömer Faruk Akgül, Yusuf Hakan Kalaycı, Rajgopal Kannan +2
Large reasoning models achieve strong performance on complex tasks by generating extended chains of thought, but they often "overthink": continuing to reason long after they have e…
HierRouter: Coordinated Routing of Specialized Large Language Models via Reinforcement Learning
Nikunj Gupta, Bill Guo, Rajgopal Kannan +1
Large Language Models (LLMs) deliver state-of-the-art performance across many tasks but impose high computational and memory costs, limiting their deployment in resource-constraine…