arXiv (All)
open access
MetaExp: Learning to Self-Improve through Experience via Verifiable Cross-Task Environment Scaling
Language-model agents can accumulate interaction feedback, but reliably turning that feedback into reusable experience remains challenging. Existing methods either optimize external experience scaffolds or train experience-related behavior on specific task distributions, leaving open whether experie
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Experience Funnel: A State-Policy Alternating Loop for Self-Evolving Agents#986540
- Environments as Scaffold: Enriching Feedback to Bootstrap Self-Evolving Agents in Long-Horizon Tasks#985760
- Self-Evolving Embodied Agents via Skill-Harness Evolution#998327
- MetaSteer: Context-Conditioned, nonlinear Steering via Attention-Projection Adaptation#1143319
- Can LLMs Provide Valid Cognitive Insight about Humans?#723598
Record · ID 1356828
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive