Tencent Open-Sources AngelSpec: A Unified Training Framework for MTP and Block-Parallel Speculative Decoding on Hy3 Models

Tencent has released AngelSpec , an open-source, torch-native training framework for speculative-decoding draft models. The release covers both autoregressive multi-token prediction (MTP) and the block-parallel DFlash family.

Most speculative-decoding work searches for one drafter that scores well on an averaged benchmark mixture. Real serving traffic does not look like that mixture. AngelSpec treats workload heterogeneity as a first-class design constraint , and specializes structure, training data, and verification depth around it.

Speculative decoding is lossless. A lightweight drafter proposes several future tokens, and the target model verifies them together in one forward pass using rejection sampling. Acceleration then depends on two things: how many draft tokens get accepted, and how long the complete draft–verify round takes.

Those two quantities move in opposite directions across domains. In high-entropy open-ended conversation, many continuations are semantically valid. The target may pick any one of them, so acceptance decays quickly with proposal depth. Generating and verifying a long block wastes compute. Autoregressive MTP drafting fits this regime because it proposes a shorter candidate sequence.

Code and mathematical reasoning behave differently. Programming syntax, repeated identifiers, formal expressions, and step-by-step derivations constrain future tokens more strongly. These workloads create longer predictable spans, which is exactly what block-parallel drafting amortizes well.

AngelSpec therefore ships two complementary drafters , not one compromise. The MTP model is trained on rich, diverse conversation-oriented data. The block-diffusion model is strengthened with code- and mathematics-focused samples.

The original Hy3 model is trained with a single MTP layer and no recurrent self-conditioned unrolling. At inference the block can be reused recurrently, but the training objective never prepared it for a long self-generated chain. Errors accumulate with depth, so the second and third draft positions accept at substantially lower rates than the first.

AngelSpec addresses this train–inference mismatch with a shared-parameter, multi-depth scheme. It retains D logical prediction depths but reuses one physical MTP block. During training that block is autoregressively unrolled for D steps, with each prediction fed into the next invocation. Following the Training-Time Test principle of EAGLE-3 , depth k+1 receives the arg max prediction from depth k instead of the ground-truth token. Parameters are shared, but supervision stays depth-specific: the teacher target advances one future position at every depth.