1 citations · 1 across the 5 of their papers we have counts for
5 papers · 1 filter
Iterative Policy Optimization: Batch-Online Multi-Turn RL via Single-Turn RL
Daniel R. Jiang, Ankur Samanta, Yukai Yang +3
Practical LLM agents often operate over multi-turn conversations where success is determined only after the full interaction ends. Most multi-turn RL methods train via on-policy ro…
CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimization
Irene Wang, Newsha Ardalani, Mostafa Elhoushi +6
Machine learning solutions are rapidly adopted to enable a variety of key use cases, from conversational AI assistants to scientific discovery. This growing adoption is expected to…
AExGym: Benchmarks and Environments for Adaptive Experimentation
Jimmy Wang, Ethan Che, Daniel R. Jiang +1
Innovations across science and industry are evaluated using randomized trials (a.k.a. A/B tests). While simple and robust, such static designs are inefficient or infeasible for tes…
Optimization-Driven Adaptive Experimentation
Ethan Che, Daniel R. Jiang, Hongseok Namkoong +1
Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization. Tailoring adaptive methods to addr…
Weakly Coupled Deep Q-Networks
Ibrahim El Shar, Daniel R. Jiang
We propose weakly coupled deep Q-networks (WCDQN), a novel deep reinforcement learning algorithm that enhances performance in a class of structured problems called weakly coupled M…