arXiv (All)
open access
VICO: Visual Environments Co-Evolving for Vision-Language Model Reasoning
Reinforcement learning with verifiable rewards (RLVR) has become a standard recipe for post-training vision-language models (VLMs), but it typically assumes a static training environment. As the actor improves, fixed tasks drift out of its learning frontier: many become trivial, others remain un
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
- Verifier-Grounded Agentic RL for Provably Correct Code#1345121
- RewardWeaver: Long-Horizon Interactive Learning for Language Agents via Self-Evolving Reward Adaptation#1234487
- Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence#1357326
- VisCritic: Visual State Comparison as Process Reward for GUI Agents#1048715
- Perception or Reasoning? Diagnosing and Tackling Reinforcement Learning Bottlenecks in VLMs#1356716
Record · ID 1247709
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