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About this paper
Shijie Xia, Yiwei Qin, Xuefeng Li, Yan Ma, Run-Ze Fan, Steffi Chern, Haoyang Zou, Fan Zhou and 6 more
Researchers from Shanghai Jiao Tong University, SII, and the Generative AI Research Lab delineate "Cognition Engineering" as the new paradigm for generative AI's "Act II," emphasizing test-time scaling methods to advance Large Language Models beyond knowledge retrieval towards deep reasoning. The work introduces a framework for understanding and implementing techniques such as parallel sampling, tree search, multi-turn correction, and long Chain-of-Thought, demonstrating their role in cultivating AI's cognitive abilities, particularly when enhanced by reinforcement learning.
Key insights
- Generative AI is transitioning into "Act II," driven by "test-time scaling" which enables Large Language Models to move from superficial knowledge retrieval to deep reasoning and thought construction.
- Online Reinforcement Learning (RL) with verifiable, rule-based rewards can unlock significant, autonomous reasoning capabilities in LLMs, demonstrating an "RL scaling phenomenon" for complex long Chain-of-Thought behaviors.
- Long Chain-of-Thought (CoT) methods, incorporating patterns like reflection and backtracking, facilitate the most human-like cognitive behaviors and achieve the highest performance for complex problems.
Summary by alphaXiv.
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