1 citations · 1 across the 9 of their papers we have counts for
10 papers
Entropy Centroids as Intrinsic Rewards for Test-Time Scaling
Wenshuo Zhao, Qi Zhu, Xingshan Zeng +4
An effective way to scale up test-time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy and Gemini Deep Think. Existi…
Teaching Large Reasoning Models Effective Reflection
Hanbin Wang, Jingwei Song, Jinpeng Li +5
Large Reasoning Models (LRMs) have recently shown impressive performance on complex reasoning tasks, often by engaging in self-reflective behaviors such as self-critique and backtr…
Group Pattern Selection Optimization: Let LRMs Pick the Right Pattern for Reasoning
Hanbin Wang, Jingwei Song, Jinpeng Li +2
Large reasoning models (LRMs) exhibit diverse high-level reasoning patterns (e.g., direct solution, reflection-and-verification, and exploring multiple solutions), yet prevailing t…
ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction
Xingshan Zeng, Weiwen Liu, Lingzhi Wang +6
Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions, often involving complex function calls and dynamic user-agent exchanges. Existi…
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs
Jierun Chen, Tiezheng Yu, Haoli Bai +11
Large vision-language models (VLMs) increasingly adopt post-training techniques such as long chain-of-thought (CoT) supervised fine-tuning (SFT) and reinforcement learning (RL) to…
ReliableMath: Benchmark of Reliable Mathematical Reasoning on Large Language Models
Boyang Xue, Qi Zhu, Rui Wang +8
Although demonstrating remarkable performance on reasoning tasks, Large Language Models (LLMs) still tend to fabricate unreliable responses when confronted with problems that are u…
EssayBench: Evaluating Large Language Models in Multi-Genre Chinese Essay Writing
Fan Gao, Dongyuan Li, Ding Xia +4
Chinese essay writing and its evaluation are critical in educational contexts, yet the capabilities of Large Language Models (LLMs) in this domain remain largely underexplored. Exi…
KDRL: Post-Training Reasoning LLMs via Unified Knowledge Distillation and Reinforcement Learning
Hongling Xu, Qi Zhu, Heyuan Deng +6
Recent advances in large language model (LLM) post-training have leveraged two distinct paradigms to enhance reasoning capabilities: reinforcement learning (RL) and knowledge disti…
How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study
Zhexin Zhang, Xian Qi Loye, Victor Shea-Jay Huang +8
Large Reasoning Models (LRMs) have achieved remarkable success on reasoning-intensive tasks such as mathematics and programming. However, their enhanced reasoning capabilities do n…
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
Yichun Yin, Wenyong Huang, Kaikai Song +49
We present Pangu Ultra, a Large Language Model (LLM) with 135 billion parameters and dense Transformer modules trained on Ascend Neural Processing Units (NPUs). Although the field…