#vla
6 articles
01
·★ MEMBER·PAPER·10 min read
Paper Explained: Beyond Data Scaling — Why the Backbone, Not the Trajectory Count, Decides Your VLA (VLAct)
Robot trajectories can't be scraped off the web, so the paper argues you should squeeze reusable representations out of the trajectories you do have. Three modest interventions — preserving the VLM prior, co-supervising with multiple action heads, and partially unifying the action space across embodiments — beat industrial VLA systems on fully open data and 16 GPUs.
02
·★ MEMBER·PAPER·10 min read
Paper Walkthrough: GigaBrain-0.7 — 37,000 Hours of Embodied Experience and a Three-System Robot Brain
A ground-up walkthrough of GigaBrain-0.7, which splits a robot foundation model into understanding, prediction/evaluation, and action, then pretrains it on 37,256.98 hours of embodied data in a single stage. Covers subgoal-image and progress-value conditioning, Soft Knowledge Insulation, and the real-robot numbers — strictly from the paper.
03
·Agents·★ MEMBER·PAPER·13 min read
Paper walkthrough: Zetta ζ — a robot harness that repairs itself mid-execution, with the policy frozen
Existing embodied agents only reflect once an episode is over. Zetta instead runs code-based critics at action frequency and intervenes the moment a failure signature appears — without touching a single policy weight. A ground-up walkthrough of the design, following the paper itself.
04
·CNNs & Image Recognition·★ MEMBER·PAPER·8 min read
Paper Walkthrough: TurboVLA — Kick the LLM Out of the Loop and Run a Robot Policy at 32 Hz on an RTX 4090 with Under 1 GB of VRAM
Mainstream VLA models route every control step through a large language model. TurboVLA removes the LLM and fuses vision and instructions with lightweight bidirectional cross-attention instead — hitting 97.7% on LIBERO with 0.2B parameters, 31.2 ms latency, and 0.9 GB of VRAM. A from-scratch walkthrough of the paper.
05
·★ MEMBER·PAPER·9 min read
Paper explained: DEFT-RLVR — show a driving VLM the future trajectory too early and it fabricates its reasoning
Let a teacher model peek at the ground-truth trajectory while writing chain-of-thought annotations for driving, and severe hallucinations double from 29% to 50% — the paper calls this trajectory anchoring bias. This article walks through AD-MCQ, which turns planning into a verifiable multiple-choice task, and DEFT-RLVR (arXiv:2608.01755), which reveals trajectories only after the model commits to a decision, using nothing but the paper itself as the source.
06
·Paper Deep-Dives·★ MEMBER·PAPER·8 min read
Paper Walkthrough: Alpamayo — NVIDIA's Reasoning Model for Autonomous Driving
A first-principles read of NVIDIA's driving VLA Alpamayo-R1: the Chain of Causation dataset that structures reasoning as cause and effect, flow-matching trajectory decoding that fits in 99 ms, and the three-stage training recipe that uses RL to punish gaps between what the model says and what it drives.