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.
Leveraging Procedural Generation to Benchmark Reinforcement Learning , July 2020
6 Pith papers cite this work, alongside 171 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
Multitask Preplay replays experience from pursued tasks as starting points for counterfactual simulation of unpursued tasks to learn predictive representations that support fast generalization in humans and machines.
CLAW is an end-to-end self-supervised method that learns semantically meaningful continuous latent actions and predictive world models from action-free videos to support imitation learning and goal-directed planning.
Empirical analysis of over 100 sequential RL training pipelines across 250+ OOD environments finds salient features drive generalization and early goals persist, with latent policy gradients simulating latent variable evolution to predict OOD behavior from training history.
PPD integrates PPO into policy distillation so the student collects and uses its own rewards, yielding better sample efficiency and robustness than standard student-distill or teacher-distill on ATARI, Mujoco, and Procgen tasks.
A Graph Attention Network pretrained solely on synthetic MDPs solves held-out tabular RL benchmarks in context, outperforming UCB-VI and Q-learning online while matching VI-LCB offline.
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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Preemptive Solving of Future Problems: Multitask Preplay in Humans and Machines
Multitask Preplay replays experience from pursued tasks as starting points for counterfactual simulation of unpursued tasks to learn predictive representations that support fast generalization in humans and machines.
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CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization
CLAW is an end-to-end self-supervised method that learns semantically meaningful continuous latent actions and predictive world models from action-free videos to support imitation learning and goal-directed planning.
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Understanding Goal Generalisation in Sequential Reinforcement Learning
Empirical analysis of over 100 sequential RL training pipelines across 250+ OOD environments finds salient features drive generalization and early goals persist, with latent policy gradients simulating latent variable evolution to predict OOD behavior from training history.
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Proximal Policy Distillation
PPD integrates PPO into policy distillation so the student collects and uses its own rewards, yielding better sample efficiency and robustness than standard student-distill or teacher-distill on ATARI, Mujoco, and Procgen tasks.
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Reinforcement Learning Foundation Models Should Already Be A Thing
A Graph Attention Network pretrained solely on synthetic MDPs solves held-out tabular RL benchmarks in context, outperforming UCB-VI and Q-learning online while matching VI-LCB offline.