2 citations · 2 across the 15 of their papers we have counts for
7 papers · 1 filter
ReDiPPO: Reference-Guided Value Calibration and Discrepancy-Aware Token Reweighting for Mathematical Reasoning
Zhenrong Zhang, Fei Wu, Jun Du +2
Reinforcement learning has emerged as an effective paradigm for enhancing the mathematical reasoning capabilities of large language models. Among existing policy optimization metho…
VLADriveBench: Evaluating CoT-Action Relationship in VLA for Autonomous Driving
Thach Nguyen, Danhua Guo, Tom Lampo +2
Vision-language-action (VLA) models generate chain-of-thought (CoT) reasoning alongside driving trajectories, but existing benchmarks evaluate only trajectory quality and do not as…
ReCal: Reward Calibration for RL-based LLM Routing
Qihang Yu, Hanwen Tong, Zhengqi Zhang +5
Large language model (LLM) routing has emerged as an effective paradigm for leveraging the complementary strengths of multiple LLMs through dynamic model and reasoning-strategy sel…
RHyVE: Competence-Aware Verification and Phase-Aware Deployment for LLM-Generated Reward Hypotheses
Feiyu Wu, Xu Zheng, Zhuocheng Wang +2
Large language models (LLMs) make reward design in reinforcement learning substantially more scalable, but generated rewards are not automatically reliable training objectives. Exi…
SteuerLLM: Local specialized large language model for German tax law analysis
Sebastian Wind, Jeta Sopa, Laurin Schmid +8
Large language models (LLMs) demonstrate strong general reasoning and language understanding, yet their performance degrades in domains governed by strict formal rules, precise ter…
Grounding Generative Planners in Verifiable Logic: A Hybrid Architecture for Trustworthy Embodied AI
Feiyu Wu, Xu Zheng, Yue Qu +3
Large Language Models (LLMs) show promise as planners for embodied AI, but their stochastic nature lacks formal reasoning, preventing strict safety guarantees for physical deployme…
Step Potential Advantage Estimation: Harnessing Intermediate Confidence and Correctness for Efficient Mathematical Reasoning
Fei Wu, Zhenrong Zhang, Qikai Chang +3
Reinforcement Learning with Verifiable Rewards (RLVR) elicits long chain-of-thought reasoning in large language models (LLMs), but outcome-based rewards lead to coarse-grained adva…