All tags
Person: "winglian"
Google's Agent2Agent Protocol (A2A)
kimi-vl-a3b gpt-4o llama-4-scout llama-4-maverick llama-4-behemoth deepcoder-14b o3-mini o1 llama-3.1-nemotron-ultra-253b deepseek-r1 google google-deepmind moonshot-ai meta-ai-fair uc-berkeley openai nvidia hugging-face togethercompute deepseek agent-interoperability multimodality vision math reinforcement-learning coding model-training open-source model-benchmarking context-windows streaming push-notifications enterprise-authentication model-release reach_vb _akhaliq epochairesearch artificialanlys winglian danielhanchen yuchenj_uw jeremyphoward
Google Cloud Next announcements featured the launch of Google and DeepMind's full MCP support and a new Agent to Agent protocol designed for agent interoperability with multiple partners. The protocol includes components like the Agent Card, Task communication channels, Enterprise Auth and Observability, and Streaming and Push Notification support. On the model front, Moonshot AI released Kimi-VL-A3B, a multimodal model with 128K context and strong vision and math benchmark performance, outperforming gpt-4o. Meta AI introduced smaller versions of llama-4 family models: llama-4-scout and llama-4-maverick, with a larger Behemoth model still in training. DeepCoder 14B from UC Berkeley is an open-source coding model rivaling openai's o3-mini and o1 models, trained with reinforcement learning on 24K coding problems. Nvidia released llama-3.1-nemotron-ultra-253b on Hugging Face, noted for beating llama-4-behemoth and maverick and competing with deepseek-r1.
TinyZero: Reproduce DeepSeek R1-Zero for $30
deepseek-r1 qwen o1 claude-3-sonnet claude-3 prime ppo grpo llama-stack deepseek berkeley hugging-face meta-ai-fair openai deeplearningai reinforcement-learning fine-tuning chain-of-thought multi-modal-benchmark memory-management model-training open-source agentic-workflow-automation model-performance jiayi-pan saranormous reach_vb lmarena_ai nearcyan omarsar0 philschmid hardmaru awnihannun winglian
DeepSeek Mania continues to reshape the frontier model landscape with Jiayi Pan from Berkeley reproducing the OTHER result from the DeepSeek R1 paper, R1-Zero, in a cost-effective Qwen model fine-tune for two math tasks. A key finding is a lower bound to the distillation effect at 1.5B parameters, with RLCoT reasoning emerging as an intrinsic property. Various RL techniques like PPO, DeepSeek's GRPO, or PRIME show similar outcomes, and starting from an Instruct model speeds convergence. The Humanity’s Last Exam (HLE) Benchmark introduces a challenging multi-modal test with 3,000 expert-level questions across 100+ subjects, where models perform below 10%, with DeepSeek-R1 achieving 9.4%. DeepSeek-R1 excels in chain-of-thought reasoning, outperforming models like o1 while being 20x cheaper and MIT licensed. The WebDev Arena Leaderboard ranks DeepSeek-R1 #2 in technical domains and #1 under Style Control, closing in on Claude 3.5 Sonnet. OpenAI's Operator is deployed to 100% of Pro users in the US, enabling tasks like ordering meals and booking reservations, and functions as a research assistant for AI paper searches and summaries. Hugging Face announces a leadership change after significant growth, while Meta AI releases the first stable version of Llama Stack with streamlined upgrades and automated verification. DeepSeek-R1's open-source success is celebrated, and technical challenges like memory management on macOS 15+ are addressed with residency sets in MLX for stability.