Beyond Environment Scaling: Effective Distributions for Multimodal Agent Learning
CASIA · hf · 2026-08-10
A new paper from CASIA points out that simply scaling up the number of multimodal environments does not always benefit agent training.
The authors tackle the problem from two dimensions: diversity and difficulty structure:
- Ability-aware Environment Selection (AES): Obtains diverse environment sets tailored to the agent's current capabilities.
- Hierarchical Difficulty Curriculum (HDC): Organizes curriculum learning via two difficulty levels: harness weakening and state-scale progression.
Experiments show that AES and HDC effectively enhance multimodal agent training outcomes.
More from coding & agent
- Deep Focus: Open-Source 'Mini Shodan' for Massive Network Asset Discovery — tom_doerr · 2026-08-10
- Developer Releases 'should-i-use' MCP Server to Drastically Cut Agent Context Usage — keyoor89 · 2026-08-10
- MOSS-Transcribe-Diarize: Transcription and Diarization in One 0.9B Model — vanstriendaniel · 2026-08-10
- Reverse Engineering Cursor and Claude Code Local Storage for Context Sync — Powerful_Language_83 · 2026-08-10
- AI SEO in Practice: Skills System Slashes Content Production to 10 Minutes — yangyi · 2026-08-10
- Goose Skills: Open-Source Library Brings Ads & SEO Capabilities to AI Coding Agents — tom_doerr · 2026-08-10