# Darwin-180B-RSI: VIDRAFT's Recursive Self-Improvement MoE Model Claims Five Leaderboard Tops > Source: > Published: 2026-09-29 23:01:18+00:00 **TL;DR:** VIDRAFT, a Korean Pre-AGI AI startup, has released Darwin-180B-RSI — a 180-billion-parameter mixture-of-experts reasoning model built on Qwen3.8-Flash-Next that combines selective model merging with recursive self-improvement. The model reportedly tops five Hugging Face leaderboards across math, science, general, and multimodal reasoning benchmarks. If the reported gains hold on task-specific workloads, it's worth a closer look for reasoning-heavy applications. Darwin-180B-RSI is VIDRAFT's open reasoning model, released in late September 2026. Here are the key technical facts as reported: The sparse expert activation design (10 of 512 experts per token routing) means that despite the 180B parameter count, the effective compute per forward pass is substantially lower than a dense 180B model — a meaningful practical consideration for deployment cost and latency. VIDRAFT describes Darwin-180B-RSI as combining two high-level techniques: **selective model merging** and **recursive self-improvement (RSI)**. At a conceptual level, the pipeline works roughly like this: This approach is conceptually related to ideas like rejection sampling fine-tuning and self-play reinforcement learning, but VIDRAFT's specific implementation details — including iteration count, reward modeling, and filtering criteria — are not disclosed in the source reporting. The MoE routing means that at inference time, each token activates only a small subset of the expert network. This is a well-established technique (see Mixtral, DeepSeek-MoE) for scaling parameter count while keeping per-token FLOPs tractable. VIDRAFT reports the following scores on publicly recognized benchmarks, with Darwin-180B-RSI claiming **first place on five Hugging Face leaderboards**: | Benchmark | Reported Score | Domain | |---|---|---| | AIME 2026 | **100% (perfect)** | Mathematical reasoning | | HMMT 2026 | **100% (perfect)** | Mathematical competition | | GPQA Diamond | **94.44%** | Graduate-level science Q&A | | MMLU-Pro | **88.12%** | Multi-domain professional knowledge | | MMMU-Pro | **79.48%** | Multimodal understanding & reasoning | ⚠️ **Important caveat:** These numbers are self-reported by VIDRAFT. The source article explicitly notes that no independent third-party validation of these results has been performed. Leaderboard rankings reflect performance on fixed benchmark datasets and may not generalize to production workloads, novel problem distributions, or domain-specific tasks. Engineers evaluating this model should run it against their own held-out test sets before drawing conclusions about real-world reasoning quality. The source reports Darwin-180B-RSI is an **open model available on Hugging Face**. If VIDRAFT follows its prior release pattern (the company had a prior Hugging Face feature covered in September), the model weights should be downloadable via the standard Hugging Face toolchain. To check availability and download weights once the repository is public: ``` # Search for the model on Hugging Face huggingface-cli search vidraft/darwin-180b-rsi # Download when available huggingface-cli download vidraft/darwin-180b-rsi ``` **Note:** The exact repository path is not confirmed in the source. Search for `VIDRAFT` or `Darwin-180B-RSI` directly on [huggingface.co](https://huggingface.co) to find the correct model card and any associated inference instructions. No GitHub repository, API endpoint, or OpenAI-compatible serving URL is mentioned in the source reporting. Check VIDRAFT's Hugging Face profile and official channels for inference API access details. **Q: Is Darwin-180B-RSI practical to self-host given its 180B parameter count?** A: The MoE architecture activates only 10 of 512 experts per request, so active-parameter FLOPs are much lower than a dense 180B model. That said, you still need sufficient memory to load all expert weights. Quantized or offloaded serving strategies (e.g., `llama.cpp`, `vLLM` with expert offloading) may be necessary depending on your hardware. Check the model card for official serving recommendations once it is published. **Q: How does the recursive self-improvement approach differ from standard RLHF or SFT?** A: Standard supervised fine-tuning (SFT) trains on human-labeled data; RLHF uses a human preference reward model. VIDRAFT's RSI loop instead uses the model's own outputs filtered by verifiable correctness — closer in spirit to rejection sampling fine-tuning or outcome-reward RL (like GRPO or STaR), but without requiring a separate reward model or human annotators for each iteration. The practical implication is that it scales more easily to domains with automated answer verification, like math and formal science. **Q: Should I trust the perfect AIME and HMMT scores at face value?** A: Treat them as a signal worth investigating, not a guarantee. Perfect scores on competition math benchmarks from self-reported results warrant independent replication. Run the model on problems from your domain and measure it yourself before committing to it for production use cases. *Originally reported by AI Market Watch (미국) (2026-09-28) — [source article](https://www.ai-market-watch.com/news/vidrafts-recursive-self-improvement-model-tops-five-global-ai-leaderboards-mctpo0).*