Re-thinking Reinforcement Learning in the Era of Large Language Models
Source
- Person key:
ysymyth - Source kind:
talk-or-slides - Canonical URL: https://docs.google.com/presentation/d/1mlhFBRdzN3aXQ1kDCwxGFfnQdjnHr7Ou9DAhLk186Y0/edit?usp=sharing&resourcekey=0-MVtkY5wr6GD-Dm80Cvsruw
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NOASSERTION - Public handling:
public-metadata-summary-hash-link-only - Semantic hash:
615a85ab41dfbbb52a22c466d8dcd82f35e542d9e3322a48b365e69dd7b8b989 - First seen: 2026-05-16
- Last changed: 2026-05-16
- Identity guard: Do not confuse with yao-shunyu-alfred, the physics-to-AI researcher at alfredyao.github.io.
Classification
- Category: Reinforcement learning and reasoning
- Topic hub: shunyu-yao-public-corpora
- Project taxonomy: shunyu-yao-project-taxonomy
- Paper map: shunyu-yao-paper-map
Summary
Talk or slides linked from the canonical homepage.
What This Teaches
How learning, planning, and feedback loops shape agent behavior and model capability.
Related
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