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Matching papers

NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness

331 upvotes · 8 SEP 2026 · NeoHorse Team, Guoliang Cao, Guohao Dai et al.

This paper proposes a method for recursive self-improvement in AI systems, where a model can learn from its own performance and use that knowledge to improve itself. Practitioners might care about this approach because it could lead to more efficient and effective AI systems.

agentic post-trainingrecursive self-improvementrouting harnessagent-native modelscontinual learning

Dream-RSI: Recursive Self-Improvement through Evolving Worlds

256 upvotes · 14 SEP 2026 · Tong Zheng, Xidong Wu, Zheng Zhang et al.

This paper introduces Dream-RSI, a framework for recursive self-improvement in exploration, which helps autonomous AI agents discover high-value solutions more efficiently by using a replay simulator to provide low-cost feedback. Practitioners might care because effective exploration is crucial for AI progress, and Dream-RSI can improve discovery quality and reduce costs.

recursive self-improvementexplorationreinforcement learningmeta-search spacesoff-policy learninglow-cost feedback

Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering

168 upvotes · 30 JUL 2026 · Junlin Yang, Che Jiang, Yu Fu et al.

This paper trains an AI model to improve itself in the process of building AI, with a focus on machine learning engineering, and shows promising results in various benchmarks. Practitioners may care about this research as it could lead to more efficient and autonomous AI development.

AI4AIrecursive self-improvementmachine learning engineeringmeta-evolutionlong-horizon searchopen-source systems

RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

70 upvotes · 14 SEP 2026 · Sibo Zhu, Shicheng Fan, Xinyue Wang et al.

This paper introduces a new framework called RSIAgent that helps digital agents adapt to new environments without needing to be retrained. A practitioner might care about this because it allows for more efficient and effective AI systems that can learn and improve on their own.

reinforcement learningmulti-agent systemsautonomous explorationrecursive self-improvementtransfer learning

Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement

59 upvotes · 11 SEP 2026 · Hongyao Tang, Yi Ma, Pengyi Li et al.

This paper proposes a single framework that can describe both iterative policy improvement and recursive self-improvement, which are key concepts in AI and machine learning, and helps analyze and design new systems that can learn and improve over time.

agent iterationpolicy improvementrecursive self-improvementformal frameworklearning paradigm