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ARXIV:2606.03927 · LLM TRAINING · SUBMITTED 03 JUN · 20:41 UTC · FRESHNESS FRESH
ARXIV:2606.03927LLM TRAININGSUBMITTED 03 JUN · 20:41 UTCFRESHNESS FRESHXinyang Liu · Xuanyu Liang · Shiqi Ding · Boyang Li · Zhiqiang Que · Jiayang Li · +1 at arXiv
A novel regression framework that adapts the biologically plausible Forward-Forward algorithm to achieve competitive accuracy with significantly reduced memory and computation compared to backpropagation.
Opportunity summary
Pain A novel regression framework that adapts the biologically plausible Forward-Forward algorithm to achieve competitive accuracy with significantly reduced memory and computation compared to backpropagation.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A novel regression framework that adapts the biologically plausible Forward-Forward algorithm to achieve competitive accuracy with significantly reduced memory and computation compared to backpropagation. However, FF is inherently designed for classification via contrastive positive-negative…
The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks through purely local, layer-wise optimization. However, FF is inherently designed for classification via contrastive positive-negative…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We propose FFR (Forward-Forward for Regression), to our knowledge, the first framework to extend FF to real-world regression and demonstrate competitive performance across diverse…
LLM Training moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
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A novel regression framework that adapts the biologically plausible Forward-Forward algorithm to achieve competitive accuracy with significantly reduced memory and computation compared to backpropagation.
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Paper Pack
10.48550/arXiv.2606.03927A novel regression framework that adapts the biologically plausible Forward-Forward algorithm to achieve competitive accuracy with significantly reduced memory and computation compared to backpropagation.
Abstract
The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks through purely local, layer-wise optimization. However, FF is inherently designed for classification via contrastive positive-negative sample pairs, and extending it to regression poses fundamental challenges: continuous target space lack natural "opposites" for contrastive learning, and the standard goodness function carries no information about target magnitude or ordering. We propose FFR (Forward-Forward for Regression), to our knowledge, the first framework to extend FF to real-world regression and demonstrate competitive performance across diverse real-world datasets. FFR introduces three key innovations: (1) an ordinal competitive goodness function that replaces contrastive pairs with competitive learning between partitioned neuron groups under distance-aware ordinal supervision; (2) a stratified ladder architecture where shallow layers learn coarse ordinal discrimination and deeper layers refine into fine-grained regression, with multi-scale feature aggregation for inter-layer collaboration; and (3) hierarchical prediction with uncertainty estimation, where multi-scale predictors jointly provide robust predictions and prediction confidence as a free-lunch. Extensive experimental results show FFR recovers on average 98.6% of BP's accuracy across five real-world regression benchmarks while reducing peak training memory to only 27% of BP's at depth 8 and 8% at depth 32, with per-iteration time around 72% of BP's, and substantially outperforms all BP-free competitors.
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Proof status
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What was readable
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Dimensions overall score 7.0
PROBLEM
A novel regression framework that adapts the biologically plausible Forward-Forward algorithm to achieve competitive accuracy with significantly reduced memory and computation compared to backpropagation. However, FF is inherently designed for classification via contrastive posi...
METHOD
The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks through purely local, layer-wise optimization. However, FF is inherently designed for classification via contrastive p...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We propose FFR (Forward-Forward for Regression), to our knowledge, the first framework to extend FF to real-world regression and demonstrate competitive performance across diverse real-world datasets. Cod...
WHY NOW
LLM Training moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 17, "author": "Xinyang Liu; Xuanyu Liang; Shiqi Ding; Boyang Li; Zhiqiang Que; Jiayang Li; Guosheng Hu", "title": "FFR: Forward-Forward Learning for Regression"
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A novel regression framework that adapts the biologically plausible Forward-Forward algorithm to achieve competitive accuracy with significantly reduced memory and computation compared to backpropagation.
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Commercial read
7.0/10 public viability
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