- paper: https://arxiv.org/pdf/2601.00417
- code added here: https://github.com/Open-Superintelligence-Lab/5-dollar-llm/pull/95/changes
Research repository dedicated to exploring and optimizing Deep Delta Learning (DDL) - a novel approach to neural architecture residual updates.
The core of this repository is the investigation of Deep Delta Residuals (DeepDeltaRes), which replaces traditional additive residuals with a learned update mechanism.
You may:
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Explain the Paper
- Translate high-level concepts and intuition into clear, narrative explanations.
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Background Math
- Deep-dive into the formal mathematical foundations and proofs.
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Implement Code
- Write clean, modular PyTorch or Triton code for the research logic.
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Training Dataset
- Build data pipelines and synthetic environments needed for experiments.
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Empirical Analysis
- Run experiments, collect insights, and visualize results.
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Peer Review
- Collaborate on existing work and provide technical feedback.
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Environment Setup:
pip install -r requirements.txt
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Run Research Baseline:
python train_llm.py
This will train a 100M parameter model using Deep Delta Learning on the base dataset.
models/deepdelta.py: Implementation of the Deep Delta Residual module.models/layers.py: Integration of DDL into Transformer blocks.train_llm.py: Main research training script.configs/: Experiment configurations for model size and hyperparameters.benchmarks/: Evaluation suite for linguistic and logic capabilities.
We welcome theoretical explorations, architectural variants, and optimization strategies for Deep Delta Learning.
- Focus on Rigor: All contributions should be backed by experimental results.
- Minimalism: We prefer clean, modular implementations that isolate the impact of DDL.
- Reproducibility: Ensure your experiments can be replicated with the provided scripts.
See Contributing Guidelines for more details.
Part of the Open Superintelligence Lab initiative.
