LIVE PULSE
4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems1 src1.3 Paper proposes evolving context parameterization for large language models1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions1 src4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems1 src1.3 Paper proposes evolving context parameterization for large language models1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions1 src
HEATPULSEAI MAGAZINES
FLIP · FOLLOW · SAVE

VLA-Precision

model1 events
papersSEP 10 04:00 UTC

VLA-Precision: Asymmetric Co-Bootstrapping for Online RL of Vision-Language-Action Models

A new arXiv paper introduces VLA-Precision, a method for fine-tuning pretrained vision-language-action models with online reinforcement learning directly on real robots. It targets manipulation tasks where such models still struggle, particularly those requiring precise, repeatable motions. The proposed asymmetric co-bootstrapping approach aims to make real-world trial-and-error learning more efficient and autonomous.