4 papers
Chained Recursive Language Models for Multi-Iteration Reasoning
Purbesh Mitra, Sennur Ulukus
Long context reasoning in large language models (LLMs) is usually constrained by the fact that a single inference trajectory has to simultaneously explore the context, store interm…
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…