Research Reproduction
Select a foundational LLM architecture or optimization paper, design a parameter- and FLOP-matched multi-seed experiment from scratch, and publish a reproducible technical report
What You Will Build
The final milestone of the AI Roadmap transitions you from an AI builder to an independent AI researcher. You will select a foundational Transformer architecture, positional encoding, normalization, or optimization paper (for example: RoPE vs. Learned/ALiBi Positional Embeddings; SwiGLU vs. GELU Feed-Forward Networks; Grouped-Query Attention vs. Multi-Head Attention; RMSNorm + QK-Norm vs. LayerNorm; or Muon/Lion vs. AdamW), translate its mathematical formulation into your own clean PyTorch implementation, design a strictly controlled experiment holding dataset order, tokenizer, parameter count, and FLOP budget constant, run both baseline and experimental variants across at least 3 random seeds (`seed=42, 43, 44`), execute a multi-row ablation table, and publish a transparent, one-command reproducible research repository and technical report.
Why This Project Matters in the AI/LLM Stack
Reading papers passively creates an illusion of understanding; reproducing a paper's claims at a scaled-down compute budget—discovering which hyperparameters actually drive the improvement, measuring seed variance ($\mu \pm \sigma$), and reporting negative or boundary regimes honestly—is the exact skill practiced at frontier AI research labs.
Learning Objectives
- Deconstruct a primary LLM research paper into its exact mathematical delta over a standard baseline.
- Design a confound-free experimental protocol matching parameter count, token budget, batch ordering, and FLOPs.
- Execute multi-seed runs (`seed=42, 43, 44`) to separate genuine architectural/ algorithmic improvements from random initialization noise.
- Design and run a systematic ablation table isolating sub-component contributions and boundary failure modes.
- Author a publication-grade technical report and a single-command `reproduce.sh` pipeline.
Prerequisites
- Phase 5 Project: Mini GPT Architecture (`milestone-10-mini-gpt-architecture`)
- Phase 6 Project: Pretrained Mini LLM (`milestone-11-pretrained-mini-llm`)
- Phase 8 Modules 8.3 & 8.5: Advanced Architectures and Research & Experimentation
Required Mathematical Concepts
- Parameter-matched architectural scaling (e.g., d_ff = (8/3) d_model for SwiGLU 3-matrix FFN vs 4 d_model for 2-matrix GELU FFN)
- Iso-FLOP training budget calculation: C ≈ 6 N D
- Multi-seed sample mean and standard deviation: mu ± sigma across K >= 3 random seeds
- Signal-to-noise ratio: |mu_exp - mu_base| / sqrt(sigma_exp^2 + sigma_base^2)
Step-by-Step Implementation Tasks (15 Steps)
Check off each engineering task as your code passes its acceptance criteria. Progress is saved automatically in your browser.
Paper Selection, Mathematical Deconstruction & Hypothesis Formulation
Select a concrete paper, isolate its exact mathematical equations, and write a falsifiable pre-registered experimental hypothesis.
A great reproduction study chooses a clear, focused paper claim that can be tested cleanly on a 5M–25M parameter Transformer.
Scientific rigor requires specifying what outcome would confirm or refute the hypothesis *before* looking at final test curves.
A naive SwiGLU layer with $d_{\text{ff}} = 4 d_{\text{model}}$ has 3 matrices instead of 2 (50% more FFN parameters than GELU!). Fair comparisons must scale $d_{\text{ff}} = \frac{2}{3}(4 d_{\text{model}})$ rounded to a multiple of 64.
Iso-Parameter & Iso-FLOP Baseline vs. Experimental Implementation
Implement the baseline and paper variant in a unified codebase, enforcing identical parameter counts, data ordering, and unit tests.
Your baseline must be well-tuned and bug-free so the proposed method does not win simply because the baseline was misconfigured.
Translate the paper's mathematical equations directly into modular PyTorch code within the same repository.
For example, RoPE dot products must depend only on relative distance $m - n$; GQA with $G = H$ must equal MHA exactly.
Every run must log its git commit hash, config JSON, random seed, hardware device, step-by-step loss/throughput telemetry, and wall-clock time.
Multi-Seed Experiments, Ablation Study & Stress Testing
Run 3 random seeds for baseline and proposed methods, execute component ablations, and test an extreme stress regime.
Running 3 independent seeds establishes the mean and standard deviation ($\mu_{\text{base}} \pm \sigma_{\text{base}}$) of the baseline.
Using the exact same 3 seeds guarantees that each seed pair (`baseline_seed42` vs `proposed_seed42`) sees the exact same mini-batch sequence.
Ablations isolate *why* the method works (e.g., in SwiGLU: testing SiLU activation alone vs. gating alone vs. full SwiGLU; in GQA: testing `kv_heads = 8, 4, 2, 1`).
Every architectural choice has tradeoffs—for example, testing length extrapolation at $2\times$ training context length, or testing stability at $3\times$ higher learning rate.
Statistical Analysis, Publication Figures & Technical Report
Compute mean ± std metrics, generate publication-quality plots, package `reproduce.sh`, and write the final research paper.
Reporting `21.42 ± 0.18` across 3 seeds shows whether an observed improvement exceeds random seed variance.
Visualizing mean validation loss curves with $\pm 1\sigma$ shaded error bands communicates convergence speed and stability immediately.
True reproducible research allows any engineer to run one script that prepares data, runs tests, executes experiments, and regenerates all tables and figures.
Structure your report like a clean ML conference workshop paper: Abstract, Paper Summary & Equations, Experimental Setup & Parity Controls, Multi-Seed Results, Ablation Study, Stress-Test & Limitations, and Reproducibility Guide.
Starter & Verification Code Examples
Iso-Parameter SwiGLU vs. GELU FFN & Multi-Seed Statistical Aggregator
Example showing how to match parameter counts between a 2-matrix GELU FFN and a 3-matrix SwiGLU FFN and compute multi-seed confidence intervals.
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class GELUFFN(nn.Module):
def __init__(self, d_model: int):
super().__init__()
d_ff = 4 * d_model
self.fc1 = nn.Linear(d_model, d_ff, bias=False)
self.fc2 = nn.Linear(d_ff, d_model, bias=False)
def forward(self, x):
return self.fc2(F.gelu(self.fc1(x)))
class IsoParamSwiGLUFFN(nn.Module):
def __init__(self, d_model: int, multiple_of: int = 32):
super().__init__()
# 2/3 of 4*d_model keeps 3-matrix parameter count equal to 2-matrix 4*d_model
hidden = int(2 * (4 * d_model) / 3)
d_ff = multiple_of * ((hidden + multiple_of - 1) // multiple_of)
self.w_gate = nn.Linear(d_model, d_ff, bias=False)
self.w_up = nn.Linear(d_model, d_ff, bias=False)
self.w_down = nn.Linear(d_ff, d_model, bias=False)
def forward(self, x):
return self.w_down(F.silu(self.w_gate(x)) * self.w_up(x))
def summarize_seeds(values: list[float]) -> str:
arr = np.asarray(values, dtype=np.float64)
return f"{arr.mean():.4f} ± {arr.std(ddof=1):.4f} (n={len(arr)})"How to Test Your Implementation
Verify parameter and data-order parity, mathematical invariants of the reproduced mechanism, and deterministic seed reproducibility.
Iso-Parameter & Data-Order Parity Test
Ensure experimental differences cannot be caused by accidental parameter bloat or different batch ordering.
Steps: Run `pytest tests/test_parity.py` comparing parameter counts and first-10-batch token IDs of baseline vs. proposed configs at `seed=42`.
Pass Criteria: Parameter delta `< 0.5%` and batch token IDs match 100%.
Deterministic Seed Reproducibility Test
Confirm that two runs with `seed=42` produce identical loss trajectories.
Steps: Run 20 training steps twice with `seed=42` on the same device and compare logged losses.
Pass Criteria: Step losses match within `1e-6`.
Statistical Aggregator Verification
Ensure `analyze_results.py` computes sample mean and standard deviation accurately.
Steps: Run `python scripts/analyze_results.py` on synthetic seed logs and verify computed `mean ± std`.
Pass Criteria: Exact match with `numpy.mean` and `numpy.std(ddof=1)`.
Overall Project Acceptance Criteria
- `verify_parity.py` confirms baseline and experimental models match parameter counts within `< 0.5%` and receive identical mini-batch sequences per seed.
- Both baseline and proposed methods are evaluated across at least 3 random seeds (`seed=42, 43, 44`) with reported `mean ± std` metrics.
- Repository includes a multi-row ablation study and a stress-regime test.
- `PAPER_REPORT.md` and `reproduce.sh` provide a complete, self-contained, reproducible research package.
Expandable Hints & Debugging Tips
Try implementing each stage on your own first. Expand a hint below if you want to verify tensor shapes, mathematical identities, or common edge cases.
Optional Stretch Challenges
Compute-Optimal Scaling Curve Comparison (3 Compute Budgets)
Compare the baseline and proposed method across 3 token budgets (e.g., 10M, 25M, 50M tokens) to measure the compute multiplier ($C_{\text{base}} / C_{\text{proposed}}$ to reach equal validation loss).
What You Learn: How frontier labs measure architectural efficiency in 'effective compute multipliers' rather than single-point loss deltas.
LaTeX Technical Report Compilation
Typeset your `PAPER_REPORT.md` into a 4–6 page NeurIPS/ICLR-style PDF paper with vector matplotlib figures and BibTeX references.
What You Learn: Academic and industrial AI research communication standards.
What Your Completed Repository Should Contain
A complete, one-command reproducible AI research repository (`reproduce.sh`) and publication-grade technical report (`PAPER_REPORT.md`) validating an empirical LLM hypothesis across multiple random seeds.
Deliverable Artifacts
- `PROPOSAL.md` with mathematical paper deconstruction and pre-registered hypothesis
- `src/models.py` & `experiments/verify_parity.py` enforcing iso-parameter and data-order parity
- Multi-seed (`seed=42, 43, 44`) experiment logs for baseline, proposed method, and ablations
- `scripts/analyze_results.py`, `scripts/plot_figures.py`, and `reproduce.sh`
- Publication-grade technical report (`PAPER_REPORT.md`) with `mean ± std` tables and figures
After Completing This Project, You Should Be Able To...
- Translate mathematical formulations from ML research papers directly into PyTorch code.
- Design rigorous iso-parameter and iso-FLOP controlled experiments free of confounding variables.
- Evaluate deep learning hypotheses statistically across multiple random seeds and ablation matrices.
- Publish transparent, reproducible AI research with candid analysis of limitations and negative results.
GitHub Repository Submission
Link your public or private GitHub repository URL to keep a structured portfolio of your from-scratch AI builds. Stored locally in your browser (self-reported; no GitHub tokens or remote code execution).
Suggested Repository: llm-research-reproduction-study
Recommended Directory Layout:
PROPOSAL.md PAPER_REPORT.md reproduce.sh src/models.py src/experiment_runner.py experiments/verify_parity.py scripts/analyze_results.py scripts/plot_figures.py figures/ tests/test_paper_mechanism.py tests/test_parity.py README.md
Recommended README Documentation Checklist
- State the reproduced paper citation and the exact hypothesis tested.
- Embed the primary multi-seed validation loss figure (`mean ± std`).
- Include the multi-row ablation and parity verification table.
- Provide the single-command `bash reproduce.sh` instructions.