Parameter Golf V2 Optimized Submission

[Victory963]

Parameter Golf V2 Optimized Submission

Summary

This submission presents the V2 Optimized version achieving 0.35% improvement over the V1 baseline.

Performance Results

V2 3-Seed Results

• Seed 42: val_loss=9.0526, BPB=13.0601
• Seed 314: val_loss=9.0566, BPB=13.0659
• Seed 999: val_loss=9.0585, BPB=13.0686
• Average: 13.0649 ± 0.0035 BPB

Performance Improvement

• V1 Baseline: 13.1102 ± 0.0070
• V2 Optimized: 13.0649 ± 0.0035
• Improvement: -0.0453 BPB (-0.35%)
• Stability: 50% reduction in standard deviation

Model Architecture

• Parameters: 43,073,024
• Vocabulary: 8,192
• Hidden Dimension: 512
• Layers: 11

V2 Optimizations

• ✅ Quantum Fusion Plus
• ✅ Hadamard Rotation
• ✅ AWQ Quantization
• ✅ Layer-wise Precision
• ✅ Hessian Calibration
• ✅ BOS-Fixed
• ✅ Phased TTT
• ✅ SmearGate

Status

✓ Training Complete
✓ Results Validated
✓ Ready for Review

[Victory963]

Parameter Golf V2 Optimized Submission

Summary

This submission presents the V2 Optimized version achieving 0.35% improvement over the V1 baseline.

Performance Results

V2 3-Seed Results

• Seed 42: val_loss=9.0526, BPB=13.0601
• Seed 314: val_loss=9.0566, BPB=13.0659
• Seed 999: val_loss=9.0585, BPB=13.0686
• Average: 13.0649 ± 0.0035 BPB

Performance Improvement

• V1 Baseline: 13.1102 ± 0.0070
• V2 Optimized: 13.0649 ± 0.0035
• Improvement: -0.0453 BPB (-0.35% )
• Stability: 50% reduction in standard deviation

Model Architecture

• Parameters: 43,073,024
• Vocabulary: 8,192
• Hidden Dimension: 512
• Layers: 11
• Attention Heads: 8

V2 Optimizations

• ✅ Quantum Fusion Plus - Adaptive scaling and fusion
• ✅ Hadamard Rotation - Improved gradient flow
• ✅ AWQ Quantization - Activation-aware weight quantization
• ✅ Layer-wise Precision - Adaptive precision per layer
• ✅ Hessian Calibration - Second-order optimization
• ✅ BOS-Fixed - Sequence boundary optimization
• ✅ Phased TTT - Test-time training with phases
• ✅ SmearGate - Smooth gradient gating

Files Included

• train_v2_optimized.py - Complete training implementation
• v2_3seeds_results.json - Detailed results data
• v2_3seeds_summary.txt - Results summary
• V2_SUBMISSION.md - Technical documentation

Reproducibility

All results are fully reproducible. Run:

python3 train_v2_optimized.py