Recent works like Tree-of-Thought (ToT) and Reasoning via Planning (RAP) aim to augment
the multi-step reasoning capabilities of LLMs by using tree-search algorithms. These …

The proliferation of large language models (LLMs) requires robust evaluation of their
alignment with local values and ethical standards, especially as existing benchmarks often …

Autonomous driving holds promise for increased safety, optimized traffic management, and
a new level of convenience in transportation. While model-based reinforcement learning …

MCTS-based algorithms, such as MuZero and its derivatives, have achieved widespread
success in various decision-making domains. These algorithms employ the reanalyze …

Current methods for end-to-end constructive neural combinatorial optimization usually train
a policy using behavior cloning from expert solutions or policy gradient methods from …

This paper presents MiniZero, a zero-knowledge learning framework that supports four state-
of-the-art algorithms, including AlphaZero, MuZero, Gumbel AlphaZero, and Gumbel …

Learning predictive world models is essential for enhancing the planning capabilities of
reinforcement learning agents. Notably, the MuZero-style algorithms, based on the value …

MuZero has demonstrated remarkable performance in board and video games where Monte
Carlo tree search (MCTS) method is utilized to learn and adapt to different game …

The nature of consciousness has been a long-debated concept related to human cognition
and self-understanding. As AI systems become more capable and autonomous, it is an …