Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
Felix Book, Arne Traue, Maximilian Schenke, Barnabas Haucke-Korber, and Oliver Wallscheid
3 Pith papers cite this work. Polarity classification is still indexing.
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CPPO is an on-policy contrastive RL method that derives advantages from contrastive Q-values for PPO optimization, outperforming prior CRL baselines in 14/18 tasks and matching or exceeding reward-based PPO in 12/18 tasks.
Gymnasium establishes a standardized API for RL environments to improve interoperability, reproducibility, and ease of development in reinforcement learning.
citing papers explorer
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Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
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Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation
CPPO is an on-policy contrastive RL method that derives advantages from contrastive Q-values for PPO optimization, outperforming prior CRL baselines in 14/18 tasks and matching or exceeding reward-based PPO in 12/18 tasks.
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Gymnasium: A Standard Interface for Reinforcement Learning Environments
Gymnasium establishes a standardized API for RL environments to improve interoperability, reproducibility, and ease of development in reinforcement learning.