Fireworks AI Launches Training Platform to Fine-Tune Frontier Models at Scale

Apr 8, 2026 · Updated Apr 25, 2026

Fireworks AI, an inference platform for fast model serving (generating outputs from trained models), launched Fireworks Training in preview. The platform supports full-parameter fine-tuning for models up to the 1-trillion-parameter Kimi K2.5. It offers three interfaces: a conversational Training Agent, managed infrastructure, and a Training API.

This shift addresses the performance ceiling teams hit with prompt engineering on closed models. By fine-tuning open-weight models like Qwen3 using reinforcement learning, companies are achieving higher accuracy and lower latency than proprietary systems. The platform also ensures numerical parity so models behave identically during training and production inference.

You can now use the Training API for custom algorithms like GRPO or the Training Agent to automate hyperparameter sweeps. Multi-LoRA serving is also available, allowing you to run multiple fine-tuned adapters on a shared base model. The platform is in preview for teams building proprietary moats.

View the full update on fireworks.ai

Fireworks AI

@FireworksAI_HQApr 7

Fireworks Training is now in preview. You can now full-parameter fine-tune Kimi K2.5 (1T params, 256k context) with custom loss functions (GRPO, DRO, DAPO, or bring your own) on managed infra. @genspark_ai built their proprietary model stack in four weeks. @vercel hit 93% error-free generation with RFT. @cursor_ai runs their RL rollout fleet on Fireworks. Full-parameter from 8B to 1T. Multi-LoRA serving. Managed or bring your own training loop. Your model is your product. Your data is your moat. https://t.co/kyz7HzihC1

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