1 citations · 2 across the 18 of their papers we have counts for
9 papers · 1 filter
One Recipe, Many Harnesses: What Self-Evolution Encodes Across Languages and Models
Siqi Yang, Qianlan Yang, Yu-Xiong Wang +2
Self-evolving harnesses are closed-loop systems in which an agent inspects its own rollouts and edits its prompts, tools, and memory. They reliably improve coding agents in evaluat…
Path-Coupled Bellman Flows for Distributional Reinforcement Learning
Boyang Xu, Qing Zou, Siqin Yang +1
Distributional reinforcement learning (DRL) models the full return distribution, but existing finite-support or quantile-based methods rely on projections, while recent flow-based…
Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence
Xuanle Zhao, Qiushi Sun, Jingyu Xiao +16
While Large Language Models (LLMs) have substantially advanced text-to-code generation, many real programming tasks specify intent through visual artifacts such as screenshots, cha…
Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
Hanxuan Chen, Jie Zheng, Siqi Yang +9
Vision-and-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) represents a pivotal challenge in embodied artificial intelligence, focused on enabling UAVs to interpret high…
Latent Wasserstein Adversarial Imitation Learning
Siqi Yang, Kai Yan, Alexander G. Schwing +1
Imitation Learning (IL) enables agents to mimic expert behavior by learning from demonstrations. However, traditional IL methods require large amounts of medium-to-high-quality dem…
ProofVerifier: A Scalable, Diversity-Driven Framework for Natural-Language Proof Verification
Haotong Yang, Zitong Wang, Shijia Kang +7
While large language models (LLMs) have achieved strong performance on mathematical problems with verifiable answers, many advanced problems are proof-based and require evaluating…
Length-Unbiased Sequence Policy Optimization: Revealing and Controlling Response Length Variation in RLVR
Fanfan Liu, Youyang Yin, Peng Shi +3
Recent applications of Reinforcement Learning with Verifiable Rewards (RLVR) to Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated significant success…
Flexible Entropy Control in RLVR with a Gradient-Preserving Perspective
Kun Chen, Peng Shi, Fanfan Liu +4
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a critical method for enhancing the reasoning capabilities of Large Language Models (LLMs). However, continuous…
V-FAT: Benchmarking Visual Fidelity Against Text-bias
Ziteng Wang, Yujie He, Guanliang Li +3
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on standard visual reasoning benchmarks. However, there is growing concern…