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GitHub Push Summary - Paper 18: Relational RNN

Push Details

Date: December 8, 2025
Repository: https://github.com/pageman/sutskever-30-implementations
Branch: main
Commits Pushed: 6 new commits

What's New

Paper 18: Relational RNN Implementation

Status: ✅ COMPLETE - Now live on GitHub

Progress Update:

  • Previous: 22/30 papers (73%)
  • Current: 23/30 papers (77%)

Commits Pushed

  1. ef4d39e - docs: Update README for Paper 18 (23/30, 77%)
  2. de78ab0 - docs: Update progress - Paper 18 complete (23/30, 77%)
  3. 3101265 - feat: Complete Paper 18 - Relational RNN implementation
  4. af18dbb - WIP: [Phase 3] Training & Baseline Comparison
  5. 7bfa739 - WIP: [Phase 2] Core Relational Memory Implementation
  6. b6a9339 - WIP: [Phase 1] Foundation & Setup

New Files on GitHub (50+)

Core Implementation:

  • 18_relational_rnn.ipynb - Main Jupyter notebook
  • attention_mechanism.py - Multi-head attention (750 lines)
  • relational_memory.py - Relational memory core (750 lines)
  • relational_rnn_cell.py - RNN cell integration (864 lines)
  • lstm_baseline.py - LSTM baseline (447 lines)
  • reasoning_tasks.py - Sequential reasoning tasks (706 lines)
  • training_utils.py - Training utilities (1,074 lines)

Training & Evaluation:

  • train_lstm_baseline.py - LSTM training script
  • train_relational_rnn.py - Relational RNN training script
  • lstm_baseline_results.json - LSTM results
  • relational_rnn_results.json - Relational RNN results
  • Training curve plots (3 PNG files)

Documentation:

  • PAPER_18_ORCHESTRATOR_PLAN.md - Implementation plan (atomic tasks)
  • PAPER_18_FINAL_SUMMARY.md - Complete summary & results
  • PHASE_3_TRAINING_SUMMARY.md - Training comparison
  • RELATIONAL_MEMORY_SUMMARY.md - Memory core details
  • RELATIONAL_RNN_CELL_SUMMARY.md - RNN cell details
  • LSTM_BASELINE_SUMMARY.md - LSTM details
  • LSTM_ARCHITECTURE_REFERENCE.md - LSTM reference
  • REASONING_TASKS_SUMMARY.md - Task descriptions
  • TRAINING_UTILS_README.md - Training utils API
  • Multiple deliverables and testing summaries

Visualizations:

  • paper18_final_comparison.png - Performance comparison
  • task_tracking_example.png - Object tracking visualization
  • task_matching_example.png - Pair matching visualization
  • task_babi_example.png - QA task visualization
  • 9 additional example visualizations

Updated Files

README.md:

  • Updated badges: 22/30 → 23/30, 73% → 77%
  • Added Paper 18 to papers table
  • Added Paper 18 to repository structure
  • Added Paper 18 to featured implementations
  • Updated "Recently Implemented" section
  • Updated completion percentage

PROGRESS.md:

  • Added Paper 18 to completed implementations
  • Removed Paper 18 from not-yet-implemented
  • Updated statistics: 22→23 implemented, 8→7 remaining
  • Updated coverage percentage: 73%→77%
  • Added to recent additions

Results

Performance Comparison

Model Test Loss Architecture
LSTM Baseline 0.2694 Single hidden state
Relational RNN 0.2593 LSTM + 4-slot memory, 2-head attention
Improvement -3.7% Better relational reasoning

Implementation Stats

  • Total Files: 50+ files (~200KB)
  • Lines of Code: 15,000+ lines
  • Tests Passed: 75+ tests (100% success rate)
  • Documentation: 10+ markdown files
  • Visualizations: 13 PNG plots

Architecture Components

✅ Multi-head self-attention mechanism
✅ Relational memory core (self-attention across slots)
✅ LSTM baseline (proper initialization)
✅ 3 sequential reasoning tasks
✅ Complete training utilities
✅ Comprehensive testing & documentation

Key Features

Educational Quality:

  • NumPy-only implementation (no PyTorch/TensorFlow)
  • Extensive inline comments and documentation
  • Step-by-step explanations
  • Comprehensive testing demonstrating correctness

Research Quality:

  • Proper LSTM initialization (orthogonal weights, forget bias=1.0)
  • Numerically stable attention implementation
  • Fair baseline comparison
  • Reproducible results

Orchestrator Framework:

  • 17 atomic tasks across 5 phases
  • Parallel execution where possible (4-8 subagents)
  • Progressive commits with clear messages
  • Complete documentation of process

What Users Can Do Now

  1. Clone the repository:

    git clone https://github.com/pageman/sutskever-30-implementations.git
cd sutskever-30-implementations

  2. Explore Paper 18:

    jupyter notebook 18_relational_rnn.ipynb

  3. Run the implementation:

    python3 train_lstm_baseline.py
python3 train_relational_rnn.py

  4. Review documentation:

    • PAPER_18_FINAL_SUMMARY.md - Overall summary
    • PAPER_18_ORCHESTRATOR_PLAN.md - Implementation plan
    • Component-specific summaries for deep dives

Next Steps

Remaining Papers (7/30):

  • Paper 8: Order Matters (Seq2Seq for Sets)
  • Paper 9: GPipe (Pipeline Parallelism)
  • Papers 19, 23, 25: Theoretical papers
  • Papers 24, 26: Course/book references

Current Progress: 77% complete - over three-quarters done!

Verification

Repository URL: https://github.com/pageman/sutskever-30-implementations

All changes are now live and publicly accessible.