RSIFlow adapts agent self-improvement with an evolving meta-agent
Original title: RSIFlow: Adaptive Self-Improvement Workflows with an Evolving Meta-Agent
Authors report state-of-the-art average scores across six benchmarks and two backbone models.
Researchers propose RSIFlow, a recursive self-improvement framework for LLM agents. It diagnoses bottlenecks from task trajectories and environmental feedback, then selects component-specific improvement workflows. A meta-agent turns outcomes into skills and principles and updates its harness for later orchestration. Ablations credit adaptive orchestration and meta-harness evolution; code is on GitHub.
Unconfirmed: State-of-the-art performance is reported by the paper's authors.
- The setting includes code generation, tool use, and multi-hop question answering.
- The meta-agent stores component-specific skills and cross-component principles.
- Ablations isolate adaptive workflow orchestration and meta-harness evolution.
- Source code is published at github.com/1HUAN1/RSI-Flow.
From the original article
1 min read at huggingface.co
Large language model (LLM) agents have demonstrated strong capabilities in complex tasks, including code generation, tool use, and multi-hop question answering. Existing self-improvement methods enhance agent performance through model optimization, harness evolution, and executable artifact refinement, while recursive self-improvement (RSI) further extends optimization to the improvement mechanisms themselves.
By Huan Zhang, Mingju Chen, Dongxu Zhou · huggingface.co
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.