djdumpling/qwen3-4b-instruct-megagem-distill-para
The djdumpling/qwen3-4b-instruct-megagem-distill-para model is a 4 billion parameter instruction-tuned language model based on the Qwen3 architecture, specifically distilled for paraphrase generation. It was developed by djdumpling using a novel ExIt training method involving self-play games and an analytic expert, resulting in improved performance against Gemini-3-Flash. This model is optimized for generating paraphrased responses, with its training incorporating a self-paraphrasing step in the blueprint's own voice.
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Model Overview
The djdumpling/qwen3-4b-instruct-megagem-distill-para is a 4 billion parameter instruction-tuned model built upon the Qwen3 architecture. It integrates a paraphrase distillation LoRA (distill1a_d1a_para) with an SFT blueprint, making it ready for deployment with standard vLLM/transformers without additional components.
Unique Training Methodology
This model employs a distinctive training approach called ExIt with an analytic expert. This method involves:
- Self-play games: The model engages in self-play to generate data.
- Analytic expert: A fitted opponent bid-distribution price law (F̂) and a self-play value head (V̂) are derived from these games.
- Closed-form best-response labels: These labels are generated on deviation turns based on expected surplus.
- Self-paraphrasing: The blueprint's own voice is used to self-paraphrase these labels, which are then used for SFT LoRA.
Notably, this training process does not rely on RL gradients or external opponent-API data for any learned component; the price law is fit exclusively on the blueprint's own self-play bids.
Performance Highlights
When evaluated against 2x Gemini-3-Flash models (paired CRN, weights-only, without a test-time selector), this model achieved a significant improvement:
- Win-rate: Increased from 29.3% to 40.7%.
- Score/game: A CV-adjusted gain of +7.4 (t≈3.0).
For deployment, an additional zero-LLM-cost analytic EV selector can be integrated, which is external code and not part of these weights, further enhancing performance against Flash/Pro/Sonnet by approximately +11–14 points.