Supervised fine-tuning (SFT) and reinforcement-learning fine-tuning (RL) both update a model’s learned parameters, or weights. The key difference is the feedback used to guide those updates: SFT trains on target answers, while RL scores answers the model generates and favors ones that score better. Neither method writes explicit rules into the model; each changes the probabilities of its future outputs through optimization.
What changes inside the model?
A language model generates text by assigning probabilities to possible next tokens given the context so far. Fine-tuning changes the model’s parameters, shifting that conditional output distribution. In practical terms, a response the model has been trained to favor becomes more likely in similar contexts; alternatives may become less likely.
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The training signal—not a simple “knowledge edit”—is the useful distinction. SFT learns from examples of desired behavior. RL-style fine-tuning learns from evaluations of generated behavior. Neither guarantees that a model will reliably acquire facts, reason better, or improve across every task; results depend on the data, reward design, model, and evaluation.
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How does each training loop work?
| Method | Training loop | What guides the update |
|---|---|---|
| SFT | Prompt → target answer → supervised loss → weight update | The target response supplied for each example |
| RL-style fine-tuning | Prompt → sampled answer(s) → reward or grade → policy update | An evaluator’s score for generated output |
A useful analogy is that SFT shows worked examples to imitate, while RL lets the model try answers and receive scores. The analogy has limits: SFT is not necessarily rote memorization, and a score does not perfectly represent answer quality.
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How does SFT teach a model?
In supervised fine-tuning, each training item pairs a prompt with a desired continuation. The training loss is tied to the target tokens; updates make the demonstrated answer more likely for similar contexts. The examples encode what a good response should look like.
This is a natural fit when the target behavior can be demonstrated directly: a particular format or tone, instruction following, classification, or a nuanced translation. The work is in creating representative prompt-and-answer examples. OpenAI’s supervised fine-tuning guide describes training on example prompts and desired outputs, and cautions that narrow or low-quality examples can lead to overfitting. It advises: “Good evals first! Only invest in fine-tuning after setting up evals.”
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How does RL-style fine-tuning use feedback?
In RL-style fine-tuning, the model generates one or more candidate answers to a prompt. A reward model, programmable grader, or other evaluator assigns feedback; an optimization procedure then updates the model’s policy to favor higher-reward outputs. The feedback might score accuracy, style, safety, or another chosen metric.
This approach can help when quality is easier to evaluate than to express as one canonical answer, or when success depends on optimizing a task metric. Its central risk is reward mismatch: if the evaluator is incomplete or flawed, the model may learn to score well without meeting the user’s real need. Evaluation should therefore measure real task performance and inspect cases or slices the grader may miss.
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RL does not mean simply generating random answers, and not every RL method uses PPO, a separate reward model, or human feedback. OpenAI’s reinforcement fine-tuning guide describes sampled outputs and programmable graders in its hosted RFT workflow. That is one implementation, not a definition of all RL research or tooling.
Can SFT and RL be used together?
Yes. One documented example is OpenAI’s 2022 InstructGPT process, which used demonstrations, preference comparisons, and reward optimization in sequence. It is a historical implementation, not a universal recipe:
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- Human-written demonstrations were used to train a supervised baseline.
- People compared model outputs; those preferences trained a reward model to predict which responses were preferred.
- The model was fine-tuned with PPO to optimize against that reward model.
The researchers noted that human preferences can represent complex, subjective goals that simple automatic metrics do not fully capture. Their paper characterized this specific procedure as using “less than 2% of the compute and data relative to model pretraining”; that figure refers to their InstructGPT work compared with GPT-3 pretraining, not to fine-tuning pipelines generally. The work also reported an “alignment tax”: gains in customer-directed behavior could come with lower performance on some academic NLP tasks. Mixing a small fraction of original pretraining data into RL fine-tuning was a mitigation in those experiments, not a guaranteed remedy for other models. See the InstructGPT paper and OpenAI’s account of aligning language models to follow instructions.
How do SFT and RL differ in practice?
| Consideration | SFT | RL-style fine-tuning |
|---|---|---|
| Signal | A desired target response for each example | A reward, grader score, or other evaluation of generated responses |
| Preparation | Curate representative prompt-and-target examples | Curate prompts and build a reliable grader, reward model, or preference signal; generate outputs to score |
| Good fit | The behavior is straightforward to demonstrate directly | Quality is easier to score than to specify with one ideal answer, or performance depends on a task metric |
| Main risk | Brittle behavior, narrow generalization, or overfitting to examples | Reward exploitation, reward mismatch, or regressions on other tasks |
| Evaluation focus | Compare held-out, representative task examples with the base model | Check both reward and actual task outcomes, including failures a grader may overlook |
These are engineering tendencies, not guarantees. A pipeline may combine supervised examples, preference learning, and reward optimization; the choice depends on what behavior is needed and what feedback can be made reliable.
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Why do fine-tuning results need careful evaluation?
A training signal can improve the behavior it measures while leaving other behavior unchanged or making it worse. SFT examples may be narrow or overfit. RL can optimize a proxy that misses important qualities, and gains on one task can coincide with regressions elsewhere. Evaluation should test held-out, representative examples against the original model, then probe failure modes and task slices that the training signal may not capture.
A 2025 preprint, “RL Is Neither a Panacea Nor a Mirage”, reports a bounded experiment using an out-of-distribution variant of the 24-point card game: RL fine-tuning recovered some SFT-related OOD loss, but did not fully recover performance after severe SFT overfitting and distribution shift. The paper’s reported scores and interventions belong to that setup; they are not general benchmark results or a guarantee about other models.
Another 2025 preprint, “SRFT”, characterizes SFT as making “coarse-grained global changes” and RL as making “fine-grained selective optimizations” to policy distributions. That is the authors’ description from their analysis, not a settled rule that applies to every model or training method.
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Bottom line: the signal steers the probabilities
Both methods change model weights. SFT uses target responses to teach demonstrated behavior; RL uses evaluations of generated responses to favor higher-scoring behavior. The result is a changed distribution of likely outputs, not a set of explicit rules. Whether that shift helps depends on the quality of the examples or evaluator and on tests that measure the behavior users actually need.
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