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Data Party THU
Data Party THU
Apr 5, 2026 · Artificial Intelligence

How Sequential World Models Enable Scalable Multi‑Robot Cooperation

SeqWM introduces a sequential causal decomposition of multi‑robot dynamics, allowing each robot to model its marginal contribution conditioned on preceding agents, which simplifies learning, improves sample efficiency, and yields natural collaborative behaviors both in simulation (Bi‑DexHands, Multi‑Quadruped) and real‑world tests on Unitree Go2‑W, outperforming prior methods.

multi-robotreal-robotreinforcement-learning
0 likes · 7 min read
How Sequential World Models Enable Scalable Multi‑Robot Cooperation
Meituan Technology Team
Meituan Technology Team
Oct 9, 2025 · Artificial Intelligence

How VSRM Cuts Redundant Reasoning Steps in Large Language Models

The paper introduces VSRM, a verifiable step‑reward mechanism that penalizes ineffective reasoning steps and rewards useful ones in large language model inference, dramatically shortening output length while preserving or even improving performance across multiple benchmarks and reinforcement‑learning algorithms.

AIEfficient Inferencelarge-language-models
0 likes · 10 min read
How VSRM Cuts Redundant Reasoning Steps in Large Language Models
Python Programming Learning Circle
Python Programming Learning Circle
May 23, 2025 · Artificial Intelligence

Useful Python Libraries for Data Science (Beyond pandas and NumPy)

This article introduces a curated list of lesser‑known Python packages for data‑science tasks—including Wget, Pendulum, imbalanced‑learn, FlashText, fuzzywuzzy, PyFlux, Ipyvolume, Dash, and Gym—providing installation commands, brief usage examples, and explanations of when each library is useful.

Pythondata-sciencemachine-learning
0 likes · 10 min read
Useful Python Libraries for Data Science (Beyond pandas and NumPy)