InclusionAI Releases Ring-2.6-1T: Trillion-Parameter Model for Agent Workflows
InclusionAI has released Ring-2.6-1T, a trillion-parameter reasoning model aimed at real-world production environments — not just benchmark chasing. The model targets agentic workflows, engineering development, scientific research, and enterprise automation where long-horizon tasks require context retention, tool use, and step planning.
Key Capabilities
- Agent execution: Moves from answering questions to executing multi-step tasks, tool collaboration, and contextual planning.
- Reasoning Effort mechanism: Two intensity levels —
highandxhigh— let developers trade off thinking depth vs. speed/cost. - Async RL training: Leverages asynchronous reinforcement learning with the IcePop algorithm to stabilize training of trillion-parameter models for long-horizon tasks.
The model is available on Hugging Face for validation and adaptation by developers, researchers, and enterprises.
📖 Read the full source: r/LocalLLaMA
👀 See Also
OneUptime adds 12,000 AI-generated blog posts in single commit
OneUptime's blog repository added 12,000 AI-generated posts covering ClickHouse, Redis, MongoDB, MySQL, and other technologies in a single commit that changed 5,012 files and over 1 million lines of code.
Analysis of 100M tokens in Claude Code reveals 99.4% input usage
Analysis of 1,289 requests across extended coding sessions shows Claude Code used 100.3M input tokens (99.4%) versus only 616K output tokens (0.6%), with 84.2M tokens cached due to repeated context re-sending.
Reddit user reports 18.8 tok/s CPU inference with Qwen 3 30B Q4 on Zen 4
A user on r/LocalLLaMA tested Qwen 3 30B Q4 on CPU and achieved 18.8 tokens per second with a Zen 4 processor and DDR5 memory, significantly exceeding expectations of 3-5 tok/s.
Talkie: A 13B LLM Trained Exclusively on Pre-1931 Text, Using Claude as a Judge in RL Training
Researchers released Talkie, a 13B LLM trained only on text published before 1931 (no internet, no WWII data). Claude Sonnet 4.6 was used as the judge in its online DPO reinforcement learning pipeline, and Claude Opus 4.4 generated synthetic multi-turn conversations for fine-tuning. The model can write Python code from a few in-context examples despite zero modern code in training.