arXiv (All)
open access
M2Tok: Multi-head Multi-codebook Discrete Action Tokenization for Vision-Language-Action Models
Recent advancements have successfully adapted autoregressive language models to process multimodal signals, such as images and actions. Since raw action signals are continuous, effective tokenization is essential to map high-dimensional inputs into compact discrete tokens for autoregressive processi
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
- CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies#927297
- RotVLA: Rotational Latent Action for Vision-Language-Action Model#1077889
- Observing and Controlling Features in Vision-Language-Action Models#1050828
- VLA Foundry: A Unified Framework for Training Vision-Language-Action Models#124125
- TOAST: Stochastic Robot Action Tokenization for Autoregressive Vision-Language-Action Models#1155834
Record · ID 1014872
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