8 papers
Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training
Chenlu Ye, Zhou Yu, Ziji Zhang +5
Reinforcement Learning with Verifiable Rewards (RLVR) improves final-answer accuracy on reasoning tasks, but it does not reliably improve reasoning quality. Because outcome rewards…
TRIM: Hybrid Inference via Targeted Stepwise Routing in Multi-Step Reasoning Tasks
Vansh Kapoor, Aman Gupta, Hao Chen +3
Multi-step reasoning tasks like mathematical problem solving are vulnerable to cascading failures, where a single incorrect step leads to complete solution breakdown. Current LLM r…
TOD-ProcBench: Benchmarking Complex Instruction-Following in Task-Oriented Dialogues
Sarik Ghazarian, Abhinav Gullapalli, Swair Shah +4
In real-world task-oriented dialogue (TOD) settings, agents are required to strictly adhere to complex instructions while conducting multi-turn conversations with customers. These…
REIC: RAG-Enhanced Intent Classification at Scale
Ziji Zhang, Michael Yang, Zhiyu Chen +6
Accurate intent classification is critical for efficient routing in customer service, ensuring customers are connected with the most suitable agents while reducing handling times a…
Semantic Volume: Quantifying and Detecting both External and Internal Uncertainty in LLMs
Xiaomin Li, Zhou Yu, Ziji Zhang +4
Large language models (LLMs) have demonstrated remarkable performance across diverse tasks by encoding vast amounts of factual knowledge. However, they are still prone to hallucina…
When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs
Xiaomin Li, Zhou Yu, Zhiwei Zhang +5
Reasoning-enhanced large language models (RLLMs), whether explicitly trained for reasoning or prompted via chain-of-thought (CoT), have achieved state-of-the-art performance on man…