3 papers
cs.CL2026
Entropy-Tree: Tree-Based Decoding with Entropy-Guided Exploration
Longxuan Wei, Yubo Zhang, Zijiao Zhang +7
Large language models achieve strong reasoning performance, yet existing decoding strategies either explore blindly (random sampling) or redundantly (independent multi-sampling). W…
cs.CL2025
Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning
Bowen Jin, TJ Collins, Donghan Yu +10
Large language models (LLMs) exhibit complementary strengths across domains and come with varying inference costs, motivating the design of multi-agent LLM systems where specialize…
cs.LG2024
Offline Reinforcement Learning for LLM Multi-Step Reasoning
Huaijie Wang, Shibo Hao, Hanze Dong +4
Improving the multi-step reasoning ability of large language models (LLMs) with offline reinforcement learning (RL) is essential for quickly adapting them to complex tasks. While D…