· ai
[Paper] Learning Sim-to-Real Humanoid Locomotion in 15 Minutes
Massively parallel simulation has reduced reinforcement learning (RL) training time for robots from days to minutes. However, achieving fast and reliable sim-to...
-
- · ai
[Paper] RoaD: Rollouts as Demonstrations for Closed-Loop Supervised Fine-Tuning of Autonomous Driving Policies
Autonomous driving policies are typically trained via open-loop behavior cloning of human demonstrations. However, such policies suffer from covariate shift whe...
- · ai
[Paper] LLM CHESS: Benchmarking Reasoning and Instruction-Following in LLMs through Chess
We introduce LLM CHESS, an evaluation framework designed to probe the generalization of reasoning and instruction-following abilities in large language models (...
- · ai
[Paper] Forecasting in Offline Reinforcement Learning for Non-stationary Environments
Offline Reinforcement Learning (RL) provides a promising avenue for training policies from pre-collected datasets when gathering additional interaction data is ...
- · ai
[Paper] A robust generalizable device-agnostic deep learning model for sleep-wake determination from triaxial wrist accelerometry
Study Objectives: Wrist accelerometry is widely used for inferring sleep-wake state. Previous works demonstrated poor wake detection, without cross-device gener...
- · ai
[Paper] Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learning
Federated Learning (FL) on resource-constrained edge devices faces a critical challenge: The computational energy required for training Deep Neural Networks (DN...
- · ai
[Paper] Chain-of-Ground: Improving GUI Grounding via Iterative Reasoning and Reference Feedback
GUI grounding aims to align natural language instructions with precise regions in complex user interfaces. Advanced multimodal large language models show strong...
- · ai
[Paper] AI-Driven Optimization under Uncertainty for Mineral Processing Operations
The global capacity for mineral processing must expand rapidly to meet the demand for critical minerals, which are essential for building the clean energy techn...
- · ai
[Paper] From Atomic to Composite: Reinforcement Learning Enables Generalization in Complementary Reasoning
The mechanism by which RL contributes to reasoning capabilities-whether it incentivizes the synthesis of new skills or merely amplifies existing behaviors-remai...
- · ai
[Paper] How Far Are We from Genuinely Useful Deep Research Agents?
Deep Research Agents (DRAs) aim to automatically produce analyst-level reports through iterative information retrieval and synthesis. However, most existing DRA...
- · ai
[Paper] Agentic Policy Optimization via Instruction-Policy Co-Evolution
Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capability of large language models (LLMs), enabling autonomous agents that can...
- · ai
[Paper] An Empirical Study of Agent Developer Practices in AI Agent Frameworks
The rise of large language models (LLMs) has sparked a surge of interest in agents, leading to the rapid growth of agent frameworks. Agent frameworks are softwa...