4 papers
Semantic Soft Bootstrapping: Long Context Reasoning in LLMs without Reinforcement Learning
Purbesh Mitra, Sennur Ulukus
Long context reasoning in large language models (LLMs) has demonstrated enhancement of their cognitive capabilities via chain-of-thought (CoT) inference. Training such models is us…
MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs
Purbesh Mitra, Sennur Ulukus
Recent advancements in the reasoning capabilities of large language models (LLMs) show that employing group relative policy optimization (GRPO) algorithm for reinforcement learning…
Distributed Mixture-of-Agents for Edge Inference with Large Language Models
Purbesh Mitra, Priyanka Kaswan, Sennur Ulukus
Mixture-of-Agents (MoA) has recently been proposed as a method to enhance performance of large language models (LLMs), enabling multiple individual LLMs to work together for collab…
Scale-Robust Timely Asynchronous Decentralized Learning
Purbesh Mitra, Sennur Ulukus
We consider an asynchronous decentralized learning system, which consists of a network of connected devices trying to learn a machine learning model without any centralized paramet…