Status: 🟡 In Progress - Training Deployed
Type: Implementation + Training Experiment
Address the catastrophic mode collapse discovered in the 1000-epoch training run, where the model generated only punctuation marks instead of coherent Shakespeare-style text. Implement comprehensive training improvements to prevent token frequency exploitation and ensure stable, quality text generation.
Status: ✅ Complete - Full Pipeline Validated
Type: Implementation + Experiment
Implement and validate the learned rounding function and custom embedding space improvements identified in our Diffusion-LM analysis, replacing cosine similarity decoding with trainable components for improved text generation quality.
Status: Complete
Type: Research
Comprehensive comparison between our current text diffusion implementation and the Diffusion-LM paper approach to identify potential improvements and architectural differences that could enhance text generation quality.
Status: Complete
Type: Baseline
Implement initial text diffusion in embedding space using Shakespeare corpus. Explore whether standard diffusion approaches can work for text generation through continuous embeddings.