27 citations · 85 across the 14 of their papers we have counts for
17 papers
Translate-R1: Cost-Aware Translation Tool Use via Reinforcement Learning
Pratik Jayarao, Chaitanya Dwivedi, Himanshu Gupta +5
The performance gap across languages in LLMs is well documented, and closing it natively requires pretraining or fine-tuning on corpora that, for most languages, are quite limited.…
AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs
Nicholas E. Corrado, Julian Katz-Samuels, Adithya Devraj +6
When aligning large language models (LLMs), their performance on various tasks (such as being helpful, harmless, and honest) depends heavily on the composition of their training da…
Gaussian Imagination in Bandit Learning
Yueyang Liu, Adithya M. Devraj, Benjamin Van Roy +1
Assuming distributions are Gaussian often facilitates computations that are otherwise intractable. We study the performance of an agent that attains a bounded information ratio wit…
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
Vivek Borkar, Shuhang Chen, Adithya Devraj +2
The paper concerns the -dimensional stochastic approximation recursion, where is a stochastic process on a general st…
A Bit Better? Quantifying Information for Bandit Learning
Adithya M. Devraj, Benjamin Van Roy, Kuang Xu
The information ratio offers an approach to assessing the efficacy with which an agent balances between exploration and exploitation. Originally, this was defined to be the ratio b…
Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation
Shuhang Chen, Adithya Devraj, Andrey Bernstein +1
The ODE method has been a workhorse for algorithm design and analysis since the introduction of the stochastic approximation. It is now understood that convergence theory amounts t…