Search Results for author: Jing-Cheng Pang

Found 7 papers, 1 papers with code

Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts

no code implementations14 Apr 2024 Jing-Cheng Pang, Si-Hang Yang, Kaiyuan Li, Jiaji Zhang, Xiong-Hui Chen, Nan Tang, Yang Yu

Furthermore, KALM effectively enables the LLM to comprehend environmental dynamics, resulting in the generation of meaningful imaginary rollouts that reflect novel skills and demonstrate the seamless integration of large language models and reinforcement learning.

Language Modelling Large Language Model +2

Empowering Language Models with Active Inquiry for Deeper Understanding

no code implementations6 Feb 2024 Jing-Cheng Pang, Heng-Bo Fan, Pengyuan Wang, Jia-Hao Xiao, Nan Tang, Si-Hang Yang, Chengxing Jia, Sheng-Jun Huang, Yang Yu

The rise of large language models (LLMs) has revolutionized the way that we interact with artificial intelligence systems through natural language.

Active Learning Language Modelling +1

Language Model Self-improvement by Reinforcement Learning Contemplation

no code implementations23 May 2023 Jing-Cheng Pang, Pengyuan Wang, Kaiyuan Li, Xiong-Hui Chen, Jiacheng Xu, Zongzhang Zhang, Yang Yu

We demonstrate that SIRLC can be applied to various NLP tasks, such as reasoning problems, text generation, and machine translation.

Language Modelling Machine Translation +3

Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation

no code implementations18 Feb 2023 Jing-Cheng Pang, Xin-Yu Yang, Si-Hang Yang, Yang Yu

To ease the learning burden of the policy, we investigate an inside-out scheme for natural language-conditioned RL by developing a task language (TL) that is task-related and unique.

Instruction Following Reinforcement Learning (RL)

Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization

no code implementations18 May 2021 Jing-Cheng Pang, Tian Xu, Shengyi Jiang, Yu-Ren Liu, Yang Yu

Reinforcement learning (RL) has made remarkable progress in many decision-making tasks, such as Go, game playing, and robotics control.

Atari Games Decision Making +3

Improving Fictitious Play Reinforcement Learning with Expanding Models

no code implementations27 Nov 2019 Rong-Jun Qin, Jing-Cheng Pang, Yang Yu

However, learning to beat a pool in stochastic games, i. e., a wide distribution over policy models, is either sample-consuming or insufficient to exploit all models with limited amount of samples.

reinforcement-learning Reinforcement Learning (RL)

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