Berkeley AI Research April 20, 2026

Gradient-based Planning for World Models at Longer Horizons

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Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Berkeley AI Research.

Berkeley AI Research

Jul 26

Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

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Berkeley AI Research

May 8

Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling

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llms rag agents nlp reinforcement-learning fine-tuning efficiency benchmarks

Berkeley AI Research

Nov 1, 2025

RL without TD learning

In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer . Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges ), and scales well to long-horizon tasks.

llms reinforcement-learning robotics benchmarks

Google AI Blog

Feb 14, 2024

Learning the importance of training data under concept drift

Posted by Nishant Jain, Pre-doctoral Researcher, and Pradeep Shenoy, Research Scientist, Google Research The constantly changing nature of the world around us poses a significant challenge for the development of AI models. Often, models are trained on longitudinal data with the hope that the training data used will accurately represent inputs the model may receive in the future.

By Google AI

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Berkeley AI Research

Jul 1

2026 BAIR Graduate Showcase

Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.

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arXiv Machine Learning

Jun 29

Qwen-Image-2.0-RL Technical Report

arXiv:2606. 27608v1 Announce Type: cross Abstract: We present Qwen-Image-2.

By Yixian Xu, Kaiyuan Gao, Yuxiang Chen, Yilei Chen, Zecheng Tang, Zihao Liu, Zikai Zhou, Deqing Li, Hao Meng, Kuan Cao, Jiahao Li, Jie Zhang, Liang Peng, Lihan Jiang, Ningyuan Tang, Shengming Yin, Tianhe Wu, Xiaoyue Chen, Yan Shu, Yanran Zhang, Yi Wang, Yu Wu, Yujia Wu, Zekai Zhang, Zhendong Wang, Xiao Xu, Kun Yan, Chenfei Wu

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Learning the importance of training data under concept drift

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