Orthrus-Qwen3-8B : up to 7.8×tokens/forward on Qwen3-8B, frozen backbone, provably identical output distribution

• Code: https://github.com/chiennv2000/orthrus
• Paper: https://arxiv.org/abs/2605.12825
• HF: https://huggingface.co/chiennv/Orthrus-Qwen3-1.7B ; https://huggingface.co/chiennv/Orthrus-Qwen3-4B ; https://huggingface.co/chiennv/Orthrus-Qwen3-8B
• Disclosure: co-author.

Idea: Inject a trainable diffusion attention module into each layer of a frozen AR Transformer. Both heads share one KV cache. Diffusion head projects K=32 tokens in parallel; AR head verifies in a second pass and accepts the longest matching prefix. Output distribution is provably identical to the base model.

Results:

• Up to 7.8× TPF, ~6× wall-clock on MATH-500.
• 16% of params trained, <1B tokens, 24h on 8×H200.
• vs. diffusion LMs (Dream, Fast-dLLM-v2, SDAR, Mercury, Gemini Diffusion): they modify base weights and lose accuracy (Fast-dLLM-v2: -11 pts on MATH-500). Orthrus freezes the backbone; accuracy matches Qwen3-8B exactly.
• vs. Speculative Decoding (EAGLE-3, DFlash): No external drafter, no separate cache, and zero Time-To-First-Token (TTFT) penalty because we don't have to initialize and sync a separate drafter model. KV overhead is O(1) (~4.5 MiB flat). Acceptance length on MATH-500: 11.7 vs. 7.9 (DFlash) vs. 3.5 (EAGLE-3).
• Single-step denoising beats multi-step (6.35 vs. 3.53 TPF). KL distillation beats CE on acceptance rate.

Limitations: strictly bounded by the frozen base model (inherits its biases, hallucinations, knowledge gaps); Qwen3-only evaluation; greedy + rejection sampling only.