Ring-1T : Best open-sourced Reasoning LLM
How to use Ring-1T for free?
At some point, the “trillion-parameter” tag stopped meaning much. Everyone has one. But Ring-1T is worth looking at, not for its size, but because it’s open-weights, training methods, reasoning traces, everything. You can grab it straight from Hugging Face or ModelScope, or talk to it via Ling Chat or ZenMux.
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Ring-1T is built for deep reasoning, trained through reinforcement learning systems that don’t just imitate human judgment but verify correctness, especially in math, code, and symbolic logic.
Architecture: Ling 2.0 on MoE
Under the hood, Ring-1T runs on Ling 2.0, a large-scale Mixture-of-Experts (MoE) architecture. MoE means the model doesn’t activate every parameter at once. Instead, for each token, a routing network selects a small subset of specialized “expert” subnetworks.
Ring-1T has 1 trillion total parameters, but only ~50 billion active per forward pass. This makes it computationally feasible while still benefiting from the parameter diversity of a trillion-scale model. The context length has been expanded to 128K tokens using YaRN, which extends sequence handling through position interpolation instead of retraining from scratch.
For training precision, there’s also a FP8 quantized version, 8-bit floating point, optimized for inference on modern GPUs. FP8 can be unstable for fine-tuning, but for inference workloads it’s fast and memory-efficient, especially on NVIDIA H100 or AMD MI300 hardware.
Training Process: RLHF + RLVR + Icepop
Ring-1T’s base model (Ling-1T-base) was pretrained on a massive multi-domain dataset. What really defines its reasoning ability, though, comes from two reinforcement learning phases:
- RLHF (Reinforcement Learning from Human Feedback) : The classic human preference fine-tuning loop, aligning the model with conversational expectations and correctness at a surface level.
- RLVR (Reinforcement Learning from Verifiable Rewards) : A stricter variant. Instead of subjective ratings, the model receives objective correctness signals from automated evaluators, mathematical proof checkers, code compilers, logic consistency tests, etc. This adds structural reasoning, not just stylistic alignment.
Where most models fall apart is in stability. Reinforcement training on MoE networks introduces inconsistencies between training-time operators (e.g., routing, activation sparsity) and inference-time execution, especially when sequence lengths or batch sizes change.
That’s where their custom algorithm, Icepop, comes in. It mitigates what they call training-inference divergence, essentially the model learning behaviors that don’t hold during real inference. Icepop uses masked bidirectional truncation to balance gradient flow during long-context updates, stabilizing RL training at scale.
The RL Infrastructure: ASystem and AReaL
Behind the curtain sits ASystem, a reinforcement learning framework built for trillion-parameter workloads. It uses a SingleController + SPMD (Single Program, Multiple Data) design, synchronized control with distributed computation across hundreds of GPUs.
It includes:
Unified memory pooling to minimize VRAM fragmentation
Transparent memory offloading for long context sequences
Direct GPU-to-GPU (P2P) communication for zero-redundant weight updates
For reward computation, Ring-1T uses AReaL, a hybrid reward engine built on a Serverless Sandbox capable of launching execution environments in milliseconds. It supports over 10 programming languages and sustains 10K RL rollouts per second, critical for scaling RLVR, since every step requires actual program execution or mathematical validation.
Both ASystem and AReaL are open-sourced, which makes this stack unusually transparent for trillion-scale models.
Benchmarking the “Thinking”
Benchmarks for reasoning models are often misleading because many of them leak into pretraining datasets. The Ring-1T team tried to counter this with semantic-level decontamination, removing benchmarks like AIME, ARC, and CodeForce from all training phases.
Even with that, the results are impressive:
These aren’t cherry-picked numbers, the team even open-sourced the solution traces for IMO and ICPC tasks, so you can inspect every reasoning step. That transparency is what gives Ring-1T an edge in the open-source space. You can literally see how it reasons, line by line.
Why It Matters
The real innovation here isn’t just “bigger model = better reasoning.” It’s the combination of stable reinforcement, verifiable correctness, and open-source transparency.
Most closed reasoning models (Gemini-Pro, GPT-5-Thinking) rely on enormous proprietary reward systems. Their outputs might look intelligent, but the underlying reward signals are hidden. Ring-1T opens that black box -down to the RL traces.
For researchers, this means reproducibility. For developers, it means you can integrate high-level reasoning into your own agents or toolchains without API dependency.
How to Try It
If you’re experimenting locally, try the FP8 version for inference, it’s about 2× faster with negligible accuracy loss. Use transformers with bitsandbytes or vLLM for efficient GPU streaming.
Final Note
The name Ring supposedly comes from circular reasoning loops, the model revisits its own conclusions until it converges. “Flow State Leads to Sudden Enlightenment” is their tagline. A bit poetic, maybe, but fitting.
For now, Ring-1T might be the closest open model to real analytical reasoning. It’s not perfect, no model is, but it’s finally one that treats reasoning as more than prompt engineering.
Ring-1T : Best open-sourced Reasoning LLM was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
