Search Results for author: Peter Chen

Found 4 papers, 1 papers with code

LLMs cannot find reasoning errors, but can correct them!

1 code implementation14 Nov 2023 Gladys Tyen, Hassan Mansoor, Victor Cărbune, Peter Chen, Tony Mak

While self-correction has shown promise in improving LLM outputs in terms of style and quality (e. g. Chen et al., 2023; Madaan et al., 2023), recent attempts to self-correct logical or reasoning errors often cause correct answers to become incorrect, resulting in worse performances overall (Huang et al., 2023).

The Importance of Sampling inMeta-Reinforcement Learning

no code implementations NeurIPS 2018 Bradly Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever

Results are presented on a new environment we call `Krazy World': a difficult high-dimensional gridworld which is designed to highlight the importance of correctly differentiating through sampling distributions in meta-reinforcement learning.

Meta Reinforcement Learning reinforcement-learning +1

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