简体中文 · English
RD-AttnRes: Residual depth routing with separate QK and V roles¶
Core mechanism reproduction. A strictly matched Block AttnRes control shares the route; the only change is a separate V route.
Paper information¶
| Field | Details |
|---|---|
| Paper | arXiv 2608.01075 |
| Institution | Kehan Wang (no institution listed) |
| First public release | 2026-08-03 (arXiv v1) |
| Author code | No official author code in the paper when checked on 2026-08-09. |
| Local adapter or method key | rd-attnres |
| Local reproduction code | src/auto_research/reproductions/rd_attnres/ |
Original paper summary¶
Background and main changes¶
Block AttnRes dynamically reads all prior residual sources, but Q, K and V share one depth route. QK handles matching while V carries content, so their depth preferences can differ. RD-AttnRes adds only one model-width route vector for V without changing residual sources or attention itself.
flowchart LR
S["All prior residual sources"] --> Q["QK depth route"]
S --> V["V depth route"]
Q --> A["Causal attention"]
V --> A
A --> R["Residual write-back and FFN"]
Key figure from the paper¶
Figure 2: Overall design and components. From the paper; copyright belongs to the authors. Click for the source.
Core equations¶
Offline and online results¶
The paper reports 2.97% and 2.43% lower perplexity than Block AttnRes at 120M and 343M parameters, respectively, winning 10/10 paired runs. No production A/B.
Local reproduction¶
Comparison: Shared-depth-route Block AttnRes versus separating QK/V only. PPL is +0.61% relative, a regression.
WikiText-2, 30 steps, seed 42: Block AttnRes PPL 316.41 and RD-AttnRes 318.33 (+0.61%, worse). JS divergence between QK and V routes is 0.00489, confirming distinct routes were learned and executed, without the paper-scale gain at this micro budget.
auto-research reproduce --paper rd-attnres --dataset-dir data --seed 42
auto-research evolve --model micro-llm --dataset wikitext-2 --direction "比较 Block AttnRes 与 RD-AttnRes 的 QK/V 深度路由" --generations 2 --population 4
See stable metrics.
Scope¶
No 120M/343M pretraining or original corpus. The local run verifies a matched architecture ablation and Evolve integration.
