9 papers
SIVA-RL: Sensitivity-Invariance Visual Alignment for Multimodal Reinforcement Learning
Cheng Tang, Junzhi Ning, Min Cen +9
Reinforcement learning with verifiable rewards (RLVR) drives multimodal reasoning, but answer-level correctness does not guarantee that a vision-language model grounds its predicti…
LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation
Zhuo Chen, Xinzhe Yuan, Jianshu Zhang +8
The high cost and data scarcity in scientific exploration have motivated the use of large language models (LLMs) as knowledge-driven components in Bayesian optimization (BO). Howev…
Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery
Xinzhe Yuan, Zhuo Chen, Jianshu Zhang +4
Scientific discovery is increasingly constrained by costly experiments and limited resources, underscoring the need for efficient optimization in AI for science. Bayesian Optimizat…
MARS: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation
Pengfei Li, Shijie Wang, Fangyuan Li +7
Reinforcement learning (RL) paradigms have demonstrated strong performance on reasoning-intensive tasks such as code generation. However, limited trajectory diversity often leads t…
WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement
Fangyuan Li, Pengfei Li, Shijie Wang +4
Recent progress in reinforcement learning with verifiable rewards (RLVR) offers a practical path to self-improvement of language models, but existing methods face a key trade-off:…
MARTI-MARS: Scaling Multi-Agent Self-Search via Reinforcement Learning for Code Generation
Shijie Wang, Pengfei Li, Yikun Fu +21
While the complex reasoning capability of Large Language Models (LLMs) has attracted significant attention, single-agent systems often encounter inherent performance ceilings in co…
Leveraging Adaptive Group Negotiation for Heterogeneous Multi-Robot Collaboration with Large Language Models
Siqi Song, Xuanbing Xie, Zonglin Li +3
Multi-robot collaboration tasks often require heterogeneous robots to work together over long horizons under spatial constraints and environmental uncertainties. Although Large Lan…
Sequential Diffusion Language Models
Yangzhou Liu, Yue Cao, Hao Li +13
Diffusion language models (DLMs) have strong theoretical efficiency but are limited by fixed-length decoding and incompatibility with key-value (KV) caches. Block diffusion mitigat…
Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, Yuhang Cao +173
In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that…