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Pulse · msn.com

msn.com · Nov 3

Taiwan's AI stock boom is making early-stage startups a harder sell

Taiwan's AI stock boom could be making it harder for young startups to win their first funding checks.

Pulse · jetstream.blog

jetstream.blog · Nov 3

Data#3 AI Practice: $3.4B Sales, FY27 Pods [2026]

Google has released the October 2026 edition of "Google AI Updates" (The latest AI news we announced in October 2026), summarizing the latest AI-related news announced last month in October 2026. The highlights for October 2026 include the general availability of the cutting-edge AI model "Gemini 4 Argon," the announcement of "Gemini 4 Carbon" (Google's most powerful AI model to date) just two weeks later, and "Genie 4," a real-time interactive world model with native audio support. Other…

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arXiv

Nov 2

Whitened Advantage Summation Policy Optimization: Correlation-Aware Aggregation for Multi-Reward Reinforcement Learning

Reinforcement learning for language models often combines several rewards by normalizing each one and adding them with fixed weights. When the rewards are correlated, the variation they share is counted once for every reward that carries it. In terms of the ZCA-decorrelated components of the reward signal, the weighted sum applies weights $Σ^{1/2} w$ rather than the chosen weights $w$, where $Σ$ is the reward correlation matrix. We study WAS-PO, which whitens the normalized advantages with $Σ^{-1/2}$ before adding them, so that the chosen weights apply to those components. We derive the scalarized objective whose gradient it follows when normalization statistics are held fixed, show that its weights on the raw rewards can be negative, give an exact two-reward condition under which the lighter-weighted reward is reversed, and give a loose bound on the error from estimating $Σ$ during training. On tool calling with Qwen2.5-1.5B-Instruct, WAS-PO reached a higher mean training reward than GDPO in all seven seeds with three rewards (3.8% higher) and all three seeds with four (3.9% higher). WAS-PO also replaces GDPO's per-prompt normalization with batch-level normalization, and we did…

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Hugging Face

Sep 11

A 12B model that rewrites AI-written drafts (emails, essays, reports, forum posts; English and Chinese) so they read like a person wrote them. It is trained to keep every number, unit, date, name and quote and to add nothing, and it runs locally.

showlab/Awesome-Multimodal-Embodied-Agent

Survey on Multimodal Embodied Agents: Computer-Use → Robot-Use

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