ERASE: EaRly bAckpropagation SchEdule for Faster Training of Modern Recommendation Systems

# EaRly bAckpropagation SchEdule for Faster Training of Modern Recommendation Systems
Source: [https://arxiv.org/html/2608.18469](https://arxiv.org/html/2608.18469)
###### Abstract

Lightweight proxy models enable rapid experimentation without repeatedly training frontier\-scale systems, but their small kernels often leave modern accelerators underutilized\. Conventional training compounds this inefficiency by scheduling the forward and backward passes as disjoint phases, so spare capacity in one cannot be filled by work from the other\. We reinterpret the detachment mechanism of Forward\-Forward \(FF\) as a scheduling primitive: given a local objective, detaching a block’s output removes downstream gradient dependencies, making its backward pass ready when its forward pass finishes\.*ERASE*launches each detached subgraph’s backward pass early on a separate CUDA stream, overlapping it with subsequent forward work\. Execution trace on a lightweight transformer demonstrates this overlap and its limit: a kernel that saturates the device leaves no capacity for concurrency\. On a large\-scale click\-through\-rate model, detaching six dense subarchitectures improves training throughput by up to9\.51%9\.51\\%while keeping normalized entropy close to the baseline\.

## 1Introduction

Backpropagation\[[21](https://arxiv.org/html/2608.18469#bib.bib19),[22](https://arxiv.org/html/2608.18469#bib.bib18)\]remains the standard method for training modern neural networks across vision and language\[[4](https://arxiv.org/html/2608.18469#bib.bib17),[26](https://arxiv.org/html/2608.18469#bib.bib16)\], agent learning and evaluation\[[18](https://arxiv.org/html/2608.18469#bib.bib15),[14](https://arxiv.org/html/2608.18469#bib.bib22),[28](https://arxiv.org/html/2608.18469#bib.bib1)\], political and social network inference\[[16](https://arxiv.org/html/2608.18469#bib.bib14),[24](https://arxiv.org/html/2608.18469#bib.bib21)\], and the natural sciences\[[20](https://arxiv.org/html/2608.18469#bib.bib13),[27](https://arxiv.org/html/2608.18469#bib.bib20)\]\. Among its most computationally demanding applications are large\-scale recommendation and ranking systems\[[2](https://arxiv.org/html/2608.18469#bib.bib12),[3](https://arxiv.org/html/2608.18469#bib.bib11)\], which are continuously retrained on data streams that can outpace a single training job\[[1](https://arxiv.org/html/2608.18469#bib.bib10),[12](https://arxiv.org/html/2608.18469#bib.bib9)\]\. Training throughput therefore directly affects model freshness and hardware resource burden\[[8](https://arxiv.org/html/2608.18469#bib.bib8)\]\.

For these workloads, throughput depends on accelerator utilization as well as peak arithmetic performance\[[29](https://arxiv.org/html/2608.18469#bib.bib7)\]\. Collective communication, data movement, and scheduling dependencies can leave capacity unused\[[17](https://arxiv.org/html/2608.18469#bib.bib6),[8](https://arxiv.org/html/2608.18469#bib.bib8)\]\. Conventional reverse\-mode differentiation traverses the graph in reverse topological order, beginning the backward pass only after the entire forward pass has completed\. The two phases therefore occupy disjoint intervals, preventing either from using spare capacity in the other\.

The same utilization pressure appears at the opposite end of the scale: frontier\-scale models require too many resources for rapid, repeated experimentation, so automated research relies on lightweight proxies such as NanoChat\[[11](https://arxiv.org/html/2608.18469#bib.bib23)\], which retains a useful experimental signal at orders\-of\-magnitude smaller scale\. The accelerator, however, does not shrink with the model\. Small GEMMs may expose too little parallelism to approach peak arithmetic throughput, leaving capacity unused even without launch gaps; CPU scheduling computational overhead can reduce utilization further when gaps occur\. We therefore ask how to improve model FLOPs utilization \(MFU\) when the model is, by design, too small to saturate the device\.

Meanwhile, the Forward\-Forward \(FF\) algorithm trains each block against a local objective and passes a*detached*activation to its successor\[[9](https://arxiv.org/html/2608.18469#bib.bib29)\]\. This partitions the end\-to\-end graph into block\-local subgraphs: gradients do not cross block boundaries, and activations need not be retained for a global backward pass\. Importantly for scheduling, a block’s backward pass becomes ready once its forward pass and local objective are complete, without waiting for the terminal loss or later blocks\. It can therefore overlap with subsequent forward passes, enabling work that the conventional forward\-then\-backward schedule forgoes\.

We turn this observation into*ERASE*\(Section[2](https://arxiv.org/html/2608.18469#S2)\), which dispatches each detached subgraph’s backward pass as soon as its forward pass returns\. Experiments \(Section[3](https://arxiv.org/html/2608.18469#S3)\) demonstrate the intended overlap and its saturation limit on NanoChat, then measure the throughput improvement on a ranking model\. Section[4](https://arxiv.org/html/2608.18469#S4)analyzes the staggering introduced by asynchronous dispatch\.

### 1\.1Related Work

##### Backpropagation\-free learning\.

Backpropagation\-free methods replace the global backward pass with block\-local targets\. NoProp and DiffusionBlocks attach an auxiliary denoising variable to each block\[[13](https://arxiv.org/html/2608.18469#bib.bib28),[25](https://arxiv.org/html/2608.18469#bib.bib5)\], borrowing their training signal from diffusion models\[[10](https://arxiv.org/html/2608.18469#bib.bib27),[23](https://arxiv.org/html/2608.18469#bib.bib26)\]; this permits detached activations and block\-local updates\. Earlier work on Forward\-Forward and layer\-wise learning likewise shows that local objectives can train competitive models without gradients crossing block boundaries\[[9](https://arxiv.org/html/2608.18469#bib.bib29),[15](https://arxiv.org/html/2608.18469#bib.bib25)\]\.

##### CUDA Graphs and execution\-level acceleration\.

CUDA Graphs capture and replay kernel sequences, reducing per\-kernel CPU launch computational overhead in small\-kernel workloads\[[19](https://arxiv.org/html/2608.18469#bib.bib2),[6](https://arxiv.org/html/2608.18469#bib.bib3)\]; recent compiler work makes capture more robust in PyTorch\[[7](https://arxiv.org/html/2608.18469#bib.bib4)\]\. ERASE combines this execution mechanism with FF\-style detachment, which creates independent pieces while preserving the usual loss and exact gradients within each piece\. Early backward launches each piece on a separate CUDA stream as soon as its forward pass returns, while CUDA Graphs keep the asynchronous launch order consistent across ranks\.

## 2Backprop Free Algorithm

### 2\.1The Forward\-Forward algorithm

##### Data dependency shutdown\.

For an input embedding𝒙∈ℝp\\bm\{x\}\\in\\mathbb\{R\}^\{p\}, consider a network ofBBblocks with𝒛0=𝒙\\bm\{z\}\_\{0\}=\\bm\{x\}, activations𝒛b\\bm\{z\}\_\{b\}, and parameters𝜽b\\bm\{\\theta\}\_\{b\}\. FF\[[9](https://arxiv.org/html/2608.18469#bib.bib29)\]computes

𝒛b=fb\(sg\[𝒛b−1\];𝜽b\),b=1,…,B,\\bm\{z\}\_\{b\}\\;=\\;f\_\{b\}\\big\(\\operatorname\{sg\}\[\\bm\{z\}\_\{b\-1\}\];\\bm\{\\theta\}\_\{b\}\\big\),\\qquad b=1,\\dots,B,\(1\)wheresg⁡\[⋅\]\\operatorname\{sg\}\[\\cdot\]is the stop\-gradient operator: it is the identity on the forward pass and has zero Jacobian on the backward pass\. A local loss on the undetached𝒛b\\bm\{z\}\_\{b\}updates𝜽b\\bm\{\\theta\}\_\{b\}alone and may, for example, apply binary cross\-entropy to the goodness scoreGb=∑jzb​j2G\_\{b\}=\\sum\_\{j\}z\_\{bj\}^\{2\}\. Consequently,∇𝜽b\\nabla\_\{\\bm\{\\theta\}\_\{b\}\}depends only on the subgraph between adjacent cut points and is ready once the block’s forward pass and local loss are complete, before the terminal loss is evaluated\. ERASE exploits this scheduling consequence of detachment; Figure[1](https://arxiv.org/html/2608.18469#S2.F1)illustrates the resulting graph\.

Figure 1:Schematic of FF\-style detachment\. Each block receives a detached copy of the previous block’s activation, and contributes its own local loss\. No gradient crosses a block boundary\.

### 2\.2Early backward in*ERASE*: scheduling, streams, and determinism

Detachment exposes independence but does not change the schedule: a single backward call on the aggregate objective

ℒ=ℒmain\+∑iλi​ℓi\\mathcal\{L\}\\;=\\;\\mathcal\{L\}\_\{\\text\{main\}\}\\;\+\\;\\sum\_\{i\}\\lambda\_\{i\}\\,\\ell\_\{i\}\(2\)still runs at the end of the step\. ERASE instead launches each detached subgraph’s backward pass as soon as its forward pass returns, accumulating its parameter gradients while leaving the undetached remainder to the usual end\-of\-step backward\. Because backward operations otherwise serialize with subsequent forward work on the default CUDA stream, ERASE uses separate streams and events to enforce the remaining dependencies\. This allows the backward pass of blockbbto overlap with the forward pass of blockb\+1b\+1\.

CPU dispatch introduces another tradeoff\. Main\-thread early backward blocks further dispatch until its autograd call is enqueued\. AThreadPoolExecutoravoids this stall but can vary the kernel and collective order across ranks, creating stragglers that we call*staggering*\. Capturing the affected subgraphs as CUDA Graphs fixes the launch order\. Section[4](https://arxiv.org/html/2608.18469#S4)compares the blocking and non\-blocking variants\.

## 3Experiments

As a small\-scale sanity check via one A100 GPU, FF\-style detachment on a3030\-layer MLP using MNIST dataset \(\[[5](https://arxiv.org/html/2608.18469#bib.bib24)\]\) reduced compute\-matched backward time by58%58\\%\(23\.523\.5to9\.99\.9ms\) and total batch time by30%30\\%\(41\.241\.2to28\.828\.8ms\)\. We then evaluate*ERASE*on NanoChat\[[11](https://arxiv.org/html/2608.18469#bib.bib23)\], using one A100 GPU to demonstrate overlap, and on a lightweight version of a large\-scale recommendation model, using eight H100 GPUs to measure throughput\.

### 3\.1NanoChat

To verify the intended overlap before experiments on a recommendation model, we partition NanoChat’s multi\-head\-attention stack into three subgraphs using two detachment points\. Figure[2](https://arxiv.org/html/2608.18469#S3.F2)shows a single\-batch execution trace\.

![Refer to caption](https://arxiv.org/html/2608.18469v1/figures/nanoChat_trace.png)Figure 2:NanoChat single\-batch trace with two detachment points and three streams\. Backward work from one subgraph runs alongside forward work from the next\.The trace demonstrates the execution pattern rather than measuring speedup: one subgraph’s backward work overlaps the next subgraph’s forward work, as Section[2\.2](https://arxiv.org/html/2608.18469#S2.SS2)predicts\. The exception is the fused multi\-head attention backward kernel \(*fm*in Figure[2](https://arxiv.org/html/2608.18469#S3.F2)\), which runs alone\. This is a resource limit rather than a dependency: by fusing the attention matrix multiplications with softmax and avoiding materialization of the sequence\-by\-sequence attention matrix, the kernel occupies every streaming multiprocessor and leaves no capacity for concurrent work\.

This limit reinforces the premise of Section[1](https://arxiv.org/html/2608.18469#S1): overlap helps only where the hardware is not already saturated\. Reducing hidden width, depth, or batch size shrinks kernels without shrinking the accelerator, leaving resources that small kernels can share but device\-filling kernels cannot\. Lightweight proxies contain mostly the former, making them natural targets for early backward\. Section[3\.2](https://arxiv.org/html/2608.18469#S3.SS2)quantifies the benefit on the recommendation model\.

### 3\.2CTR Model

#### 3\.2\.1Setup

The remaining experiments use a click\-through\-rate \(CTR\) model with six detached subarchitectures, four of which are assigned separate CUDA streams alongside the main stream\. Throughput is the number of training examples processed per second \(QPS\), summarized by the post\-warm\-upp​90p90of per\-step samples \(higher is better\); unlike latencyp​90p90, this is the fast end of the distribution\. Quality is normalized entropy \(NE\), the model’s cross\-entropy divided by that of a constant predictor; lower is better\.

#### 3\.2\.2Early backward on the CTR model

ERASE launches each detached subarchitecture’s backward pass from aThreadPoolExecutoras soon as its forward pass completes\. Because the worker thread can introduce cross\-rank launch\-order nondeterminism, we capture a subset of the subarchitectures as CUDA Graphs to fix their order and reduce CPU dispatch computational overhead\. This non\-blocking configuration \(row 1 of Table[1](https://arxiv.org/html/2608.18469#S4.T1)\) improves throughput by7\.38%7\.38\\%with an NE gap of approximately1\.38%1\.38\\%\(Figure[3](https://arxiv.org/html/2608.18469#S3.F3)\)\. Section[4](https://arxiv.org/html/2608.18469#S4)examines its FUP=False setting and compares it with blocking dispatch\.

![Refer to caption](https://arxiv.org/html/2608.18469v1/figures/NE_similar_shape_zoom_wo_y.png)Figure 3:NE gap between the non\-blocking early\-backward variant with CUDA Graphs \(red curve\) and the baseline \(blue curve\), approximately1\.38%1\.38\\%\.

## 4Caveats

Table[1](https://arxiv.org/html/2608.18469#S4.T1)reports dispatch and FUP \(find\_unused\_parameters\) ablations\.*Blocking*launches early backward from the main thread and stalls CPU dispatch during autograd enqueue; FUP controls which parameters participate in gradient synchronization\.

Table 1:Throughput on the CTR model,p​90p90queries per secondBlocking is deterministic but stalls the CPU: FUP=False gains only0\.37%0\.37\\%\(row 3\), whereas FUP=True gains5\.26%5\.26\\%\(row 2\) by reducing the parameters in the final aggregate backward\. CUDA Graphs instead recover a7\.38%7\.38\\%gain with non\-blocking dispatch and FUP=False \(row 1\)\. Together, these results suggest that deterministic, rank\-synchronized collective order is useful and that either FUP or CUDA Graphs can provide it\.

## 5Conclusion

*ERASE*repurposes FF\-style detachment as a scheduling primitive: cutting inter\-block dependencies makes each backward pass ready after its forward pass, enabling separate\-stream overlap while preserving exact within\-subgraph gradients without FF’s goodness objective\. On a CTR model, ERASE improvesp​90p90QPS by55–9%9\\%with a small NE gap under deterministic cross\-rank collective order\.

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