All tags
Topic: "agentic-benchmarks"
DeepSeek V3.1: 840B token continued pretrain, beating Claude 4 Sonnet at 11% of its cost
deepseek-v3.1 seed-oss-36b computerrl gemini-2.5-pro gpt-5 claude-code gpt-oss-120b gpt-oss-20b deepseek bytedance zhipu-ai github microsoft anthropic together-ai baseten huggingface token-efficiency coding agentic-benchmarks long-context reinforcement-learning developer-tools fine-tuning multinode-training model-release teortaxestex rasbt lukehoban burkeholland _catwu cline winglian
DeepSeek released DeepSeek V3.1, a quietly rolled out open model with an 128K context window and improvements in token efficiency, coding, and agentic benchmarks. ByteDance launched the permissive Seed-OSS 36B model on Hugging Face, noted for long-context and reasoning capabilities. Zhipu AI introduced ComputerRL, a reinforcement learning framework for computer-use agents, achieving strong benchmark results. In developer tooling, GitHub Copilot expanded globally, Microsoft VS Code integrated Gemini 2.5 Pro and updated GPT-5 agent prompts, and Anthropic launched Claude Code seats with spend controls. Open-source fine-tuning advances include Together AI adding SFT for gpt-oss-120B/20B and Baseten enabling multinode 120B training with Truss CLI. The community noted mixed performance and ongoing post-training adjustments for DeepSeek V3.1.
not much happened today
deepseek-r1-0528 o3 gemini-2.5-pro claude-opus-4 deepseek_ai openai gemini meta-ai-fair anthropic x-ai ollama hugging-face alibaba bytedance xiaomi reasoning reinforcement-learning benchmarking quantization local-inference model-evaluation open-weights transparency post-training agentic-benchmarks long-context hallucination-detection teortaxestex wenfeng danielhanchen awnihannun reach_vb abacaj
DeepSeek R1-0528 release brings major improvements in reasoning, hallucination reduction, JSON output, and function calling, matching or surpassing closed models like OpenAI o3 and Gemini 2.5 Pro on benchmarks such as Artificial Analysis Intelligence Index, LiveBench, and GPQA Diamond. The model ranks #2 globally in open weights intelligence, surpassing Meta AI, Anthropic, and xAI. Open weights and technical transparency have fueled rapid adoption across platforms like Ollama and Hugging Face. Chinese AI labs including DeepSeek, Alibaba, ByteDance, and Xiaomi now match or surpass US labs in model releases and intelligence, driven by open weights strategies. Reinforcement learning post-training is critical for intelligence gains, mirroring trends seen at OpenAI. Optimized quantization techniques (1-bit, 4-bit) and local inference enable efficient experimentation on consumer hardware. New benchmarks like LisanBench test knowledge, planning, memory, and long-context reasoning, with OpenAI o3 and Claude Opus 4 leading. Discussions highlight concerns about benchmark contamination and overemphasis on RL-tuned gains.