5 citations · 14 across the 8 of their papers we have counts for
6 papers · 1 filter
MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling
Jiacheng Chen, Xinyu Zhang, Shunkai Zhang +20
We present MaxProof, a population-level test-time scaling framework for competition-level mathematical proof in the MiniMax-M3 series. M3 first trains three proof-oriented capabili…
Local Environment Poisoning Attacks on Federated Reinforcement Learning
Evelyn Ma, Praneet Rathi, S. Rasoul Etesami
Federated learning (FL) has become a popular tool for solving traditional Reinforcement Learning (RL) tasks. The multi-agent structure addresses the major concern of data-hungry in…
Theta-Resonance: A Single-Step Reinforcement Learning Method for Design Space Exploration
Masood S. Mortazavi, Tiancheng Qin, Ning Yan
Given an environment (e.g., a simulator) for evaluating samples in a specified design space and a set of weighted evaluation metrics -- one can use Theta-Resonance, a single-step M…
The Role of Local Steps in Local SGD
Tiancheng Qin, S. Rasoul Etesami, César A. Uribe
We consider the distributed stochastic optimization problem where agents want to minimize a global function given by the sum of agents' local functions, and focus on the hetero…
Faster Convergence of Local SGD for Over-Parameterized Models
Tiancheng Qin, S. Rasoul Etesami, César A. Uribe
Modern machine learning architectures are often highly expressive. They are usually over-parameterized and can interpolate the data by driving the empirical loss close to zero. We…
Communication-efficient Decentralized Local SGD over Undirected Networks
Tiancheng Qin, S. Rasoul Etesami, César A. Uribe
We consider the distributed learning problem where a network of agents seeks to minimize a global function . Agents have access to through noisy gradients, and they can…