feat: MMMU Integration and Mathematical Reasoning Enhancements

[devin-ai-integration]

Generative-Flex Model Improvement Recommendations

Current Performance Analysis

Strengths

1. Calculus Performance (78.57% accuracy)

   • Balanced distribution of problem difficulty (5 easy, 5 medium, 1 hard)
   • Strong performance despite complexity of subject matter
   • Represents significant portion (36.67%) of validation set

2. General Mathematical Reasoning (71.43% overall)

   • Consistent performance across varied problem types
   • Handles medium difficulty problems well
   • Demonstrates robust base mathematical capabilities

Areas Requiring Improvement

1. Geometry (64.29% accuracy)

   • Lowest performing category
   • Limited sample size (5 problems)
   • All problems are easy or medium difficulty
   • Potential issues with spatial reasoning or geometric visualization

2. Hard Problem Performance

   • Limited exposure to hard problems (only 5 total)
   • Concentrated in "Other" category (4 hard problems)
   • Need for more challenging problem exposure

Recommended Improvements

1. Training Data Enhancements

• Geometry-Specific Augmentation

  • Increase geometry problems in training set
  • Add more complex geometric reasoning tasks
  • Include problems requiring visual/spatial reasoning
  • Focus on coordinate geometry and proofs

• Difficulty Balance

  • Increase proportion of hard problems across all categories
  • Maintain balanced distribution within categories
  • Add more challenging calculus problems

2. Model Architecture Adjustments

• Spatial Reasoning Enhancement

  • Add dedicated geometry-focused attention heads
  • Implement specialized geometric embedding layer
  • Consider adding visual reasoning components

• Problem Difficulty Handling

  • Implement difficulty-aware attention mechanism
  • Add complexity-based routing in mixture of experts
  • Enhance mathematical symbol processing

3. Training Optimizations

• Learning Rate Adjustments

  • Implement category-specific learning rates
  • Use larger learning rates for geometry training
  • Apply curriculum learning based on problem difficulty

• Batch Composition

  • Ensure balanced category representation in batches
  • Gradually increase problem difficulty during training
  • Implement geometry-focused training phases

4. Evaluation Improvements

• Enhanced Metrics

  • Track performance by problem difficulty
  • Monitor category-specific learning curves
  • Implement geometric reasoning specific metrics

• Validation Set Enhancement

  • Add more geometry problems to validation set
  • Ensure balanced difficulty distribution
  • Include more hard problems across categories

Implementation Priority

1. Immediate Actions

   • Implement geometry-focused attention heads
   • Adjust batch composition for better category balance
   • Add more geometry problems to training set

2. Short-term Improvements

   • Deploy difficulty-aware attention mechanism
   • Implement category-specific learning rates
   • Enhance validation metrics

3. Long-term Enhancements

   • Develop specialized geometric reasoning components
   • Create comprehensive curriculum learning system
   • Build advanced performance monitoring tools

Expected Outcomes

After implementing these improvements, we expect:

1. Geometry performance to increase to ~75% accuracy
2. More consistent performance across problem difficulties
3. Better handling of hard problems across all categories
4. Improved overall mathematical reasoning capabilities

Monitoring and Validation

To ensure improvements are effective:

1. Track category-specific performance metrics
2. Monitor learning curves for each difficulty level
3. Validate improvements on held-out test sets
4. Conduct periodic performance audits

This improvement plan focuses on addressing the identified weaknesses while maintaining and building upon the model's current strengths in calculus and general mathematical reasoning.

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