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VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models
arXiv:2609.04355v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) models enable broad manipulation but remain unreliable in tasks demanding precision and repeatability. Applying real-world online reinforcement learning (RL) to VLA post-training enables autonomous trial-and-error improvement beyond demonstrations alone, but exposes two bottlenecks: 1) unreliable value signals can induce policy drift; 2) large-VLA overhead constrains throughput and sample efficiency. To address these challenges, we present VLA-Precision, an efficient real-world online RL framework featuring the Asymmetric Co-Bootstrapping (ACoB) algorithm and the ACoB-Stream architecture. Specifically, ACoB establishes asymmetric co-bootstrapping across timescales: early intervention-guided behavioral learning rapidly improves policy performance while enhancing online experience quality. As autonomous experience accumulates, global return propagation and local preference ranking progressively calibr
Text Watermarking in Python: Catch Whoever Copies Your Writing
AI companies quietly watermark billions of words a day. Here’s how to apply the same three families of techniques to your own writing—and what real experiments reveal about which watermarks survive copy-paste, editing, and paraphrasing. The post Text Watermarking in Python: Catch Whoever Copies Your Writing appeared first on Towards Data Science.
Linear Discriminant Analysis (LDA) in Real-Life: Dimensionality Reduction in a Real-Estate Dataset
Using LDA for dimensionality reduction in classification problems The post Linear Discriminant Analysis (LDA) in Real-Life: Dimensionality Reduction in a Real-Estate Dataset appeared first on Towards Data Science.
Why Transformers Need Positional Encoding For Time Series: A Visual Guide
From scalar observations to self-attention, and how positional information restores sequence order The post Why Transformers Need Positional Encoding For Time Series: A Visual Guide appeared first on Towards Data Science.
Dynamical System Transfer Learning with Reduced Order Models
Improving reinforcement learning for complex physics The post Dynamical System Transfer Learning with Reduced Order Models appeared first on Towards Data Science.
GPT-6 Astra: What’s Actually New in OpenAI’s New Frontier Model
OpenAI has released GPT-6 Astra, its newest frontier model, less than a week after Anthropic s Claude Fable 5.1. OpenAI calls Astra the world s most intelligent and aligned model yet. What actually makes Astra different? The simplest way to put it is this: Astra is built to do more, not just answer more. It can use [ ] The post GPT-6 Astra: What s Actually New in OpenAI s New Frontier Model appeared first on Analytics Vidhya.
Optimal Traffic Allocation Under Heterogeneous Variant Cost
Why the default 50/50 split is the wrong move when your treatment is more expensive than your control, and how cost-based sampling weights fix it The post Optimal Traffic Allocation Under Heterogeneous Variant Cost appeared first on Towards Data Science.
Disaggregation Is a Thousand-GPU Problem
Three conditions that must hold before splitting prefill from decode pays off, and why chunked prefill is the right default below that threshold. The post Disaggregation Is a Thousand-GPU Problem appeared first on Towards Data Science.
The Power BI Developer's Survival Guide to Microsoft Fabric
Power BI Premium is gone. Microsoft Fabric took its place. Here's what actually changed for you, what didn't, and where to start — without the panic. The post The Power BI Developer's Survival Guide to Microsoft Fabric appeared first on Towards Data Science.
How to Run 10+ Claude Code Sessions Without a Powerful Computer
Learn how to run a lot of parallel coding agents without expensive, powerful hardware at home The post How to Run 10+ Claude Code Sessions Without a Powerful Computer appeared first on Towards Data Science.
Top 10 GitHub Repositories Trending in August 2026 (AI, Agents & Dev Tooling Edition)
If you spent any time on GitHub Trending in August, you probably noticed the centre of gravity had shifted again. Models took a back seat to the machinery around them: agent harnesses, skills, memory layers, gateways, and document tooling. One repository alone gained more than 190,000 stars in four weeks. We tracked star growth, momentum, ecosystem impact, [ ] The post Top 10 GitHub Repositories Trending in August 2026 (AI, Agents Dev Tooling Edition) appeared first on Analytics Vidhya.
MasterControl Seventeen Every Time
arXiv:2609.03209v1 Announce Type: new Abstract: We study a governed approach to enterprise analytics: a language model interprets the question, while deterministic policy selects and runs a pre-approved analytical program that returns both results and evidence. We show that this restriction can remain expressive within a defined analytical class, using relational operations plus aggregation, comparison, windows, ranking, and similarity. Fixed meaning, policy, data, and execution rules also make results replayable. Across 440 runs, three 8B models generated SQL and selected tools at runtime, while Qwen3-8B interpreted intent only and policy executed the approved program. None of 330 runtime-planning episodes matched the full answer-and-evidence contract across all test datasets; the policy-executed analyzer matched 110 of 110. This is a configuration-specific result, not evidence that runtime agents cannot succeed under other designs.