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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Go-explore: a new approach for hard-exploration problems
17 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
Decision Transformer casts RL as autoregressive sequence modeling conditioned on desired returns, past states and actions, matching or exceeding offline RL baselines on Atari, Gym and Key-to-Door tasks.
TriSearch is an RL framework that optimizes triangulations of polytopes using bistellar flips with a circuit-supported subtriangulation action representation, generalizing zero-shot to larger instances and outperforming prior samplers in 3D and 4D.
Alice uses preservation conflicts from failed candidate updates to create class-stratified hypotheses and guide exploration, improving executable world-model learning under prior misalignment.
Voyager achieves superior lifelong learning in Minecraft by combining an automatic exploration curriculum, a library of executable skills, and iterative LLM prompting with environment feedback, yielding 3.3x more unique items and 15.3x faster milestone unlocks than prior methods while generalizing技能
DAERT generates diverse adversarial instructions via a uniform policy in RL to drop VLA task success rates from 93.33% to 5.85% on benchmarks with models like π0 and OpenVLA.
CoALA is a modular cognitive architecture for language agents that organizes memory components, action spaces for internal and external interaction, and a generalized decision-making loop to support more systematic development of capable agents.
A two-stage framework learns a world graph of pivotal states task-agnostically via joint training of a latent model and curiosity-driven policy, then uses the graph to accelerate hierarchical RL on maze tasks.
RL researchers need to separate 'solving simulators' from 'using simulators as proxies' to prevent misleading conclusions about algorithm performance.
Mesh-RL applies finite-element-inspired domain decomposition with overlapping subgrids to accelerate temporal-difference learning across distant states in grid-world environments.
RQGM enables co-evolution of agents and evaluators across epochs with non-stationary utilities, reporting gains in coding pass rates, paper acceptance, and proof grading over prior self-improving agents.
ConTrack introduces a constrained RL method with online dual-variable adaptation and adaptive resets for improved long-horizon hand tracking in simulation and on real robots.
Reflector internalizes step-wise self-reflection in LLMs via teacher-guided SFT then RL with outcome and validity rewards, claiming over 90% defense success against indirect jailbreaks plus utility gains like 5.85% on GSM8K.
DyGRO-VLA is a two-stage optimization framework for cross-task scaling of Vision-Language-Action models via dynamic grouped residual optimization in RL.
Mind Dreamer uses active causal intervention via an adversarial initial-state generator and relay value functions to untether imagination in MBRL, claiming 1.67x average and up to 8.8x sparse-reward speedups over DreamerV3.
PokeRL trains PPO agents to finish early Pokemon Red tasks using a loop-aware environment wrapper, multi-layer anti-loop mechanisms, and dense hierarchical rewards.
OPPO augments PPO with optimistic policy evaluation driven by return uncertainty estimates and shows improved results over prior methods on a tabular sparse-reward task.
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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Decision Transformer: Reinforcement Learning via Sequence Modeling
Decision Transformer casts RL as autoregressive sequence modeling conditioned on desired returns, past states and actions, matching or exceeding offline RL baselines on Atari, Gym and Key-to-Door tasks.
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TriSearch: Learning to Optimize Triangulations via Bistellar Flips
TriSearch is an RL framework that optimizes triangulations of polytopes using bistellar flips with a circuit-supported subtriangulation action representation, generalizing zero-shot to larger instances and outperforming prior samplers in 3D and 4D.
-
Baba in Wonderland: Online Self-Supervised Dynamics Discovery for Executable World Models
Alice uses preservation conflicts from failed candidate updates to create class-stratified hypotheses and guide exploration, improving executable world-model learning under prior misalignment.
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Voyager: An Open-Ended Embodied Agent with Large Language Models
Voyager achieves superior lifelong learning in Minecraft by combining an automatic exploration curriculum, a library of executable skills, and iterative LLM prompting with environment feedback, yielding 3.3x more unique items and 15.3x faster milestone unlocks than prior methods while generalizing技能
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Uncovering Linguistic Fragility in Vision-Language-Action Models via Diversity-Aware Red Teaming
DAERT generates diverse adversarial instructions via a uniform policy in RL to drop VLA task success rates from 93.33% to 5.85% on benchmarks with models like π0 and OpenVLA.
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Cognitive Architectures for Language Agents
CoALA is a modular cognitive architecture for language agents that organizes memory components, action spaces for internal and external interaction, and a generalized decision-making loop to support more systematic development of capable agents.
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Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
A two-stage framework learns a world graph of pivotal states task-agnostically via joint training of a latent model and curiosity-driven policy, then uses the graph to accelerate hierarchical RL on maze tasks.
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Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy
RL researchers need to separate 'solving simulators' from 'using simulators as proxies' to prevent misleading conclusions about algorithm performance.
-
Mesh-RL: Coupled subgrid reinforcement learning
Mesh-RL applies finite-element-inspired domain decomposition with overlapping subgrids to accelerate temporal-difference learning across distant states in grid-world environments.
-
The Red Queen G\"odel Machine: Co-Evolving Agents and Their Evaluators
RQGM enables co-evolution of agents and evaluators across epochs with non-stationary utilities, reporting gains in coding pass rates, paper acceptance, and proof grading over prior self-improving agents.
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ConTrack: Constrained Hand Motion Tracking with Adaptive Trade-off Control
ConTrack introduces a constrained RL method with online dual-variable adaptation and adaptive resets for improved long-horizon hand tracking in simulation and on real robots.
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REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak
Reflector internalizes step-wise self-reflection in LLMs via teacher-guided SFT then RL with outcome and validity rewards, claiming over 90% defense success against indirect jailbreaks plus utility gains like 5.85% on GSM8K.
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DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization
DyGRO-VLA is a two-stage optimization framework for cross-task scaling of Vision-Language-Action models via dynamic grouped residual optimization in RL.
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Mind Dreamer: Untethering Imagination via Active Causal Intervention on Latent Manifolds
Mind Dreamer uses active causal intervention via an adversarial initial-state generator and relay value functions to untether imagination in MBRL, claiming 1.67x average and up to 8.8x sparse-reward speedups over DreamerV3.
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PokeRL: Reinforcement Learning for Pokemon Red
PokeRL trains PPO agents to finish early Pokemon Red tasks using a loop-aware environment wrapper, multi-layer anti-loop mechanisms, and dense hierarchical rewards.
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Optimistic Proximal Policy Optimization
OPPO augments PPO with optimistic policy evaluation driven by return uncertainty estimates and shows improved results over prior methods on a tabular sparse-reward task.