14 citations · 23 across the 10 of their papers we have counts for
6 papers · 1 filter
Learning Robust Social Strategies with Large Language Models
Dereck Piche, Mohammed Muqeeth, Milad Aghajohari +3
As agentic AI becomes more widespread, agents with distinct and possibly conflicting goals will interact in complex ways. These multi-agent interactions pose a fundamental challeng…
The Markovian Thinker: Architecture-Agnostic Linear Scaling of Reasoning
Milad Aghajohari, Kamran Chitsaz, Amirhossein Kazemnejad +4
Reinforcement learning (RL) has recently become a strong recipe for training reasoning LLMs that produce long chains of thought (LongCoT). Yet the standard RL "thinking environment…
VinePPO: Refining Credit Assignment in RL Training of LLMs
Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance +4
Large language models (LLMs) are increasingly applied to complex reasoning tasks that require executing several complex steps before receiving any reward. Properly assigning credit…
Advantage Alignment Algorithms
Juan Agustin Duque, Milad Aghajohari, Tim Cooijmans +4
Artificially intelligent agents are increasingly being integrated into human decision-making: from large language model (LLM) assistants to autonomous vehicles. These systems often…
Meta-Value Learning: a General Framework for Learning with Learning Awareness
Tim Cooijmans, Milad Aghajohari, Aaron Courville
Gradient-based learning in multi-agent systems is difficult because the gradient derives from a first-order model which does not account for the interaction between agents' learnin…
Riemannian Diffusion Models
Chin-Wei Huang, Milad Aghajohari, Avishek Joey Bose +2
Diffusion models are recent state-of-the-art methods for image generation and likelihood estimation. In this work, we generalize continuous-time diffusion models to arbitrary Riema…