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New framework ARISE-RL enables iterative self-evolution for AI agents

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced ARISE-RL, a novel framework designed to enhance the self-evolution capabilities of agents trained with reinforcement learning. This framework addresses challenges in open-ended tasks by coupling a task/rubric generator with a reasoning solver, enabling iterative improvement. It also incorporates Reward-Gated Self-Evolution Distillation (RG-SED) to refine policy learning and introduces ECR-Bench, a new benchmark suite for evaluating agent performance on complex research and planning tasks. AI

IMPACT This research introduces a framework for agents to iteratively improve their capabilities, potentially leading to more robust and adaptable AI systems.

RANK_REASON The cluster describes a new research framework and benchmark suite published on arXiv.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework ARISE-RL enables iterative self-evolution for AI agents

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The cluster describes a new research framework and benchmark suite published on arXiv.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Fanrui Zhang, Ruixue Ding, Qiang Zhang, Xi Chen, Boli Chen, Shihang Wang, Qiuchen Wang, Hongmin Zhan, Jinxin Bian, Li xingchao, Peijin Zheng, Hao cheng, Pengjun Xie, Kaipeng Zhang, Jiawei Liu, Zheng-Jun Zha ·

    ARISE-RL: Agentic Rubric-Grounded Iterative Self-Evolution with Reinforcement Learning

    arXiv:2609.01058v1 Announce Type: new Abstract: Training open-ended agents via reinforcement learning (RL) is hindered by the lack of verifiable gold answers and scalable rubrics. Moreover, even near the model's capability boundary, long-horizon open-ended agentic tasks often yie…

  2. dev.to — LLM tag TIER_1 中文(ZH) · Sanya ·

    A Panoramic Review of Agent Self-Evolution Capabilities: From One-Shot Learning to Continuous Growth (2023-2025)

    <h1> Agent 自我进化能力全景综述：从一次学会到持续成长（2023-2026） </h1> <blockquote> <p><strong>摘要：</strong> 大语言模型（LLM）本质上是一套静态系统——训练完成，能力定格。但现实世界的任务从不会自我重复。当 Agent 开始具备"知道自己不知道什么"并主动改造自己的能力时，AI 的范式正在发生根本性转变。本文系统梳理 2023-2026 年 Agent 自我进化的核心方法，围绕"<strong>进化什么</strong>""<strong>何时进化</strong>""<strong>如何…

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