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What is reinforcement fine-tuning?

In traditional supervised fine-tuning, you provide a dataset with labeled examples showing exactly what the model should output. In reinforcement fine-tuning, you instead provide:
  1. A dataset: Prompts, with input examples for the model to respond to
  2. An evaluator: Code that scores the model’s outputs from 0.0 (bad) to 1.0 (good), also known as a reward function
  3. An environment: The system where your agent runs, with access to tools, APIs, and data needed for your task
During training, the model generates responses to each prompt, receives scores from your reward function, and produces outputs that maximize the reward.

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Use cases

Reinforcement fine-tuning helps you train models to excel at:
  • Code generation and analysis - Writing and debugging functions with verifiable execution results or test outcomes
  • Structured output generation - JSON formatting, data extraction, classification, and schema compliance with programmatic validation
  • Domain-specific reasoning - Legal analysis, financial modeling, or medical triage with verifiable criteria and compliance checks
  • Tool-using agents - Multi-step workflows where agents call external APIs with measurable success criteria

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How it works

1

Design your evaluator

Define how you’ll score model outputs from 0 to 1. For example, scoring outputs higherchecking if your agent called the right tools, or if your LLM-as-judge rates the output highly.
2

Prepare dataset

Create a JSONL file with prompts (system and user messages). These will be used to generate rollouts during training.
3

Connect your environment

Train locally, or connect your environment as a remote server to Fireworks with our /init and /status endpoints.
4

Launch training

Create an RFT job via the UI or CLI. Fireworks orchestrates rollouts, evaluates them, and trains the model to maximize reward.
5

Deploy model

Once training completes, deploy your fine-tuned LoRA model to production with an on-demand deployment.

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RFT works best when:

  1. You can determine whether a model’s output is “good” or “bad,” even if only approximately
  2. You have prompts but lack perfect “golden” completions to learn from
  3. The task requires multi-step reasoning where evaluating intermediate steps is hard
  4. You want the model to explore creative solutions beyond your training examples

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Next steps

Create an evaluator

Learn how to design effective reward functions

Kick off training

Learn how to launch and configure RFT jobs