arXiv Machine Learning
Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction
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How Lossless Is Lossless Speculative Decoding? The Role of Numerical Precision in Orthrus
Orthrus is a hybrid autoregressive‑diffusion model that claims to perform lossless speculative decoding by generating multiple tokens in parallel while maintaining a frozen autoregressive backbone. In our independent reproduction, we found that under BF16 precision the model matches the exact autoregressive trajectory only about 45% of the time, whereas with FP32 precision it achieves perfect matching on all evaluated prompts. Despite trajectory mismatches at lower precision, Orthrus does not exhibit systematic degradation on downstream lm‑eval‑harness benchmarks.