7 papers · 1 filter
Boosting RL-Based Visual Reasoning with Selective Adversarial Entropy Intervention
Yang Yu, Zhuangzhuang Chen, Lanqing Li +1
Recently, reinforcement learning (RL) has become a common choice in enhancing the reasoning capabilities of vision-language models (VLMs). Considering existing RL-based finetuning…
Pass@k Metric for RLVR: A Diagnostic Tool of Exploration, But Not an Objective
Yang Yu
The ability of Large Language Models (LLMs) to perform complex, multi-step reasoning is a central focus of modern AI research. To evaluate and enhance this capability, the pass@k m…
Reinforcement Learning with Anticipation: A Hierarchical Approach for Long-Horizon Tasks
Yang Yu
Solving long-horizon goal-conditioned tasks remains a significant challenge in reinforcement learning (RL). Hierarchical reinforcement learning (HRL) addresses this by decomposing…
LLM Data Selection and Utilization via Dynamic Bi-level Optimization
Yang Yu, Kai Han, Hang Zhou +4
While large-scale training data is fundamental for developing capable large language models (LLMs), strategically selecting high-quality data has emerged as a critical approach to…
CCI4.0: A Bilingual Pretraining Dataset for Enhancing Reasoning in Large Language Models
Guang Liu, Liangdong Wang, Jijie Li +6
We introduce CCI4.0, a large-scale bilingual pre-training dataset engineered for superior data quality and diverse human-like reasoning trajectory. CCI4.0 occupies roughly TB…
DTVM: Revolutionizing Smart Contract Execution with Determinism and Compatibility
Wei Zhou, Xiong Xu, Changzheng Wei +22
We introduce the DeTerministic Virtual Machine (DTVM) Stack, a next-generation smart contract execution framework designed to address critical performance, determinism, and ecosyst…
NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios
Songyi Gao, Zuolin Tu, Rong-Jun Qin +3
Offline reinforcement learning (RL) aims to learn from historical data without requiring (costly) access to the environment. To facilitate offline RL research, we previously introd…