Efficient Exploration Is Enough

• URL: http://arxiv.org/abs/2609.07575v1
• Date: Mon, 07 Sep 2026 14:52:36 GMT
• Title: Efficient Exploration Is Enough
• Abstract summary: We study efficient exploration through the lens of prediction and generalization.<n>We show that optimally efficient explorers naturally schedule their trajectories to visit the most informative and learnable regions first.<n>We believe that this new framework provides a principled mechanism by which agent-environment systems may sustain an open-ended process of increasingly complex behavior without external rewards, tasks, or objectives.
• Score: 12.168912356703016
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work introduces an alternative view of efficient exploration and studies its theoretical and empirical implications in the absence of extrinsic rewards. Specifically, we define efficient explorers as agents that prioritize generating generalizable experience, i.e., data that supports learning models capable of predicting and adapting across the environment. This allows us to analyze efficient exploration through the lens of prediction and generalization. Theoretically, we demonstrate that optimally efficient explorers naturally schedule their trajectories to visit the most informative and learnable regions first. Empirically, we show that optimizing for these agents gives rise to an automatic curriculum of progressively more complex behaviors, even in relatively simple environments. These results indicate that pursuing this purely intrinsic objective alone is enough to drive the emergence of highly sophisticated behaviors. We believe that this new framework provides a principled mechanism by which agent-environment systems may sustain an open-ended process of increasingly complex behavior without external rewards, tasks, or objectives.

Related papers

• Look Before You Leap: Autonomous Exploration for LLM Agents [46.001025916022066]
  Large language model based agents often fail in unfamiliar environments due to premature exploitation.<n>We identify autonomous exploration as a critical yet underexplored capability for building adaptive agents.
  arXiv  Detail & Related papers  (2026-05-15T16:24:16Z)
• Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy [0.5805874695844994]
  We develop a deep-learning BEACON framework to guide discovery in the target space.<n>This benchmarking framework provides a basis for evaluating discovery-driven algorithms.<n>The associated notebooks are available, allowing users to reproduce, test the benchmarks, and adapt the method to their own instruments and datasets.
  arXiv  Detail & Related papers  (2026-03-17T16:04:20Z)
• Towards Agentic Intelligence for Materials Science [73.4576385477731]
  This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
  arXiv  Detail & Related papers  (2026-01-29T23:48:43Z)
• AgentEvolver: Towards Efficient Self-Evolving Agent System [51.54882384204726]
  We present AgentEvolver, a self-evolving agent system that drives autonomous agent learning.<n>AgentEvolver introduces three synergistic mechanisms: self-questioning, self-navigating, and self-attributing.<n>Preliminary experiments indicate that AgentEvolver achieves more efficient exploration, better sample utilization, and faster adaptation compared to traditional RL-based baselines.
  arXiv  Detail & Related papers  (2025-11-13T15:14:47Z)
• Agent Learning via Early Experience [93.83579011718858]
  A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks.<n>Most current agents rely on supervised fine-tuning on expert data, which is challenging to scale and generalizes poorly.<n>We study two strategies of using such data: (1) Implicit world modeling, which uses collected states to ground the policy in environment dynamics; and (2) Self-reflection, where the agent learns from its suboptimal actions to improve reasoning and decision-making.
  arXiv  Detail & Related papers  (2025-10-09T17:59:17Z)
• Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling [11.478146371965984]
  We propose a practical, theoretically grounded approach to optimistic exploration based on Thompson sampling.<n>Our experiments demonstrate that optimistic exploration significantly accelerates learning in environments with sparse rewards.<n> Furthermore, we provide insights into when optimism is beneficial and emphasize the critical role of model uncertainty in guiding exploration.
  arXiv  Detail & Related papers  (2024-10-07T12:42:51Z)
• Self-supervised network distillation: an effective approach to exploration in sparse reward environments [0.0]
  Reinforcement learning can train an agent to behave in an environment according to a predesigned reward function. The solution to such a problem may be to equip the agent with an intrinsic motivation that will provide informed exploration. We present Self-supervised Network Distillation (SND), a class of intrinsic motivation algorithms based on the distillation error as a novelty indicator.
  arXiv  Detail & Related papers  (2023-02-22T18:58:09Z)
• Online reinforcement learning with sparse rewards through an active inference capsule [62.997667081978825]
  This paper introduces an active inference agent which minimizes the novel free energy of the expected future. Our model is capable of solving sparse-reward problems with a very high sample efficiency. We also introduce a novel method for approximating the prior model from the reward function, which simplifies the expression of complex objectives.
  arXiv  Detail & Related papers  (2021-06-04T10:03:36Z)
• Understanding the origin of information-seeking exploration in probabilistic objectives for control [62.997667081978825]
  An exploration-exploitation trade-off is central to the description of adaptive behaviour. One approach to solving this trade-off has been to equip or propose that agents possess an intrinsic 'exploratory drive' We show that this combination of utility maximizing and information-seeking behaviour arises from the minimization of an entirely difference class of objectives.
  arXiv  Detail & Related papers  (2021-03-11T18:42:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.