Reinforcement Fine-Tuning

Fireworks RFT helps you train frontier models like DeepSeek V3 and Kimi K2 to outperform closed models for your product use case, using reinforcement learning. Fireworks RFT is powerful and easy to use for developers and enterprises:

• No infrastructure: Train frontier models without managing GPUs or RL infra
• Production-ready: Built-in tracing, monitoring, security & one-click deploy
• Fast iteration: From evaluator setup to deployed model in hours, not weeks

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Quickstart: pick your training approach

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How RFT works

In supervised fine-tuning you provide labeled examples of good outputs. In RFT you provide prompts and an evaluator (reward function) that scores model outputs from 0 to 1. Training iteratively improves outputs that maximize that score. RFT fits when the task is verifiable (you can judge output quality), you lack golden completions, or the task needs multi-step reasoning.

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Launch and operate

Use the docs for path choice and quickstarts. Use the Fireworks training skill for launch flags, validation, monitoring, and troubleshooting.

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Prerequisites

Confirm account access and quota, an RFT-compatible base model, valid prompt JSONL, and a reviewed evaluator that produces non-flat scores on a small probe.

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Evaluators

Evaluators turn a rollout into a reward. Test full-credit, partial-credit, zero, malformed-output, and edge cases locally before registration. Keep evaluator and preference-data guidance in the evaluator skill reference.

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Parameter tuning

Resolve the current defaults from firectl rftj create --help and the live RFT parameter reference. Start with a small run, change one optimization or rollout variable at a time, and compare reward and held-out quality.

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Weighted training

Use per-example or per-token weighting only when the selected managed contract supports it. Inspect the rendered samples and effective loss mask before launch.

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Launch from the CLI

Use firectl rftj create for a reproducible managed job. Prepare the full base-model, dataset, evaluator, and output-model resource names, run the documented preflight, and confirm the resolved plan before creation.

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Launch from the dashboard

Use the dashboard for a guided first run: choose the model, dataset, evaluator, rollout settings, and optional W&B project, then review the summary before launch.

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Warm start

Warm-start from a compatible promoted LoRA when continuing SFT into RFT. Do not also supply a conflicting base-model field; verify the current CLI contract before creation.

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Evaluator secrets

Store credentials in the Fireworks secret-management surface and reference them by environment-variable name. Never embed secret values in evaluator code, datasets, or run reports.

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Cost planning

Estimate rollout inference, training runtime or tokens, candidate count, response length, and any deployment uptime. Use the live pricing page and Price vs Tinker; do not assume a free tier.

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Monitor training

Track state plus actual progress: optimizer steps, rollout throughput, reward distribution, validation results, logs, and W&B metrics when enabled. Inspect representative rollouts for reward hacking and stop when quality regresses or the evaluator saturates. Parameter field reference: RFT parameters (API tab).

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