6 papers
Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers
Xin-Qiang Cai, Wei Wang, Feng Liu +3
Reinforcement Learning with Verifiable Rewards (RLVR) replaces costly human labeling with automated verifiers. To reduce verifier hacking, many RLVR systems binarize rewards to $\{…
Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding
Zhiqin Yang, Yuhan Liu, Jingwen Fu +4
Although natural language is the default medium for Large Language Models (LLMs), its limited expressive capacity creates a profound bottleneck for complex problem-solving. While r…
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
Xingjun Ma, Yifeng Gao, Yixu Wang +45
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artifici…
AlphaApollo: A System for Deep Agentic Reasoning
Zhanke Zhou, Chentao Cao, Xiao Feng +15
We present AlphaApollo, an agentic reasoning system that targets two bottlenecks in foundation-model reasoning: (1) limited reasoning capacity for complex, long-horizon problem sol…
BrokenBind: Universal Modality Exploration beyond Dataset Boundaries
Zhuo Huang, Runnan Chen, Bo Han +3
Multi-modal learning combines various modalities to provide a comprehensive understanding of real-world problems. A common strategy is to directly bind different modalities togethe…
Bifrost: Steering Strategic Trajectories to Bridge Contextual Gaps for Self-Improving Agents
Quan M. Tran, Zhuo Huang, Wenbin Zhang +4
Autonomous agents excel in self-improvement through reflection and iterative refinement, which reuse successful task trajectories as in-context examples to assist subsequent reason…