Gen-1 Slides: Opus 5-level decks at a fraction of the cost

Genspark and Fireworks Lab post-trained the open-weight MiniMax M3 model into Gen-1 Slides, a specialized agentic system designed to plan, create, render, review, and revise presentation decks across long, multi-turn workflows. The partners treated slide generation as a reinforcement learning challenge because high-quality decks require visual judgment, self-correction, and credit assignment across trajectories that can exceed 100,000 tokens, rather than simple imitation of finished examples. Genspark defined production-based quality standards and evaluation criteria covering design, layout failures, and factual issues, while Fireworks Lab developed training infrastructure, reward engineering, and stability measures through more than 100 experiments. Training used a staged curriculum that began with supervised fine-tuning on curated decks, then expanded RL context lengths as the model improved, while addressing numerical problems such as tokenization mismatches between inference and training systems that could bias updates. According to Genspark’s internal evaluation, Gen-1 Slides matches or exceeds Opus 5 on most quality measures while costing roughly one-seventeenth as much per input token and about 90% less per completed deck, reducing low-rated production decks from 18% to 3.6% relative to the base model.

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