31 citations · 58 across the 11 of their papers we have counts for
7 papers · 1 filter
Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Information
Priyank Agrawal, Ankur Samanta, Shervin Ghasemlou +5
Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models. Yet, typical RLVR approaches fail on difficult problems: when a model cannot gene…
Iterative GRPO: Batch-Online Multi-Turn RL via Single-Turn RLHF
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…
Aligned Multi Objective Optimization
Yonathan Efroni, Ben Kretzu, Daniel Jiang +4
To date, the multi-objective optimization literature has mainly focused on conflicting objectives, studying the Pareto front, or requiring users to balance tradeoffs. Yet, in machi…
Pearl: A Production-ready Reinforcement Learning Agent
Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari +12
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals. Although many real-world problems can be formalized with RL, learning and deploying a performan…
On Linear Convergence of Policy Gradient Methods for Finite MDPs
Jalaj Bhandari, Daniel Russo
We revisit the finite time analysis of policy gradient methods in the one of the simplest settings: finite state and action MDPs with a policy class consisting of all stochastic po…
Global Optimality Guarantees For Policy Gradient Methods
Jalaj Bhandari, Daniel Russo
Policy gradients methods apply to complex, poorly understood, control problems by performing stochastic gradient descent over a parameterized class of polices. Unfortunately, even…
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation
Jalaj Bhandari, Daniel Russo, Raghav Singal
Temporal difference learning (TD) is a simple iterative algorithm used to estimate the value function corresponding to a given policy in a Markov decision process. Although TD is o…