10 papers
ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders
Zhongyuan Peng, Dan Huang, Chuyu Zhang +8
The recent emergence of vibe-coding workflows is changing what coding agents are expected to do. Instead of merely completing code under fully specified instructions, agents are in…
DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes
Caijun Xu, Changyi Xiao, Zhongyuan Peng +1
Reinforcement learning has become a central paradigm for advancing reasoning in large language models, yet most existing methods still depend on stronger teacher models or heavily…
SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning
Caijun Xu, Changyi Xiao, Zhongyuan Peng +2
Reinforcement learning (RL) offers a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on training signals that remain inf…
Reinforcement Learning with Conditional Expectation Reward
Changyi Xiao, Caijun Xu, Yixin Cao
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing the reasoning capabilities of large language models, particularly in domains such as mathema…
NEX: Neuron Explore-Exploit Scoring for Label-Free Chain-of-Thought Selection and Model Ranking
Kang Chen, Zhuoka Feng, Sihan Zhao +5
Large language models increasingly spend inference compute sampling multiple chain-of-thought traces or searching over merged checkpoints. This shifts the bottleneck from generatio…
CoDiQ: Test-Time Scaling for Controllable Difficult Question Generation
Zhongyuan Peng, Caijun Xu, Changyi Xiao +4
Large Reasoning Models (LRMs) benefit substantially from training on challenging competition-level questions. However, existing automated question synthesis methods lack precise di…