4 papers
When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning
Ruotao Xu, Yixin Ji, Yu Luo +5
Large reasoning models (LRMs) have achieved strong performance enhancement through scaling test time computation, but due to the inherent limitations of the underlying language mod…
When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling
Zhimin Lin, Yixin Ji, Jinpeng Li +5
Large Reasoning Models (LRMs) achieve strong performance on mathematical reasoning tasks but remain unreliable on challenging instances. Existing test-time scaling methods, such as…
Ratio-Variance Regularized Policy Optimization for Efficient LLM Fine-tuning
Yu Luo, Shuo Han, Yihan Hu +2
On-policy reinforcement learning (RL), particularly Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), has become the dominant paradigm for fine-tuni…
Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition
Hanting Chen, Yasheng Wang, Kai Han +21
This work presents Pangu Embedded, an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs), featuring flexible fast and slow thinking ca…