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Both supervised fine-tuning (SFT) and reinforcement-learning (RL) fine-tuning change a model’s learned parameters, or weights. The difference is the signal driving those updates: SFT trains on example answers, while RL trains on model-generated answers that receive a reward or grader score. In both cases, optimization changes the probabilities of future outputs; neither method simply writes explicit rules into the model.
What changes inside the model?
A language model produces text by assigning probabilities to possible next tokens given the preceding context. Fine-tuning adjusts its parameters so that this conditional output distribution changes. With SFT, the model learns from target responses supplied in training examples. With RL-style fine-tuning, it generates responses and an evaluator scores them; the training procedure then favors higher-scoring behavior.
The exact update depends on the algorithm and implementation. The useful distinction is the training signal—not whether weights change. Both methods update weights.
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The loop: prompt, target, supervised loss, update
In SFT, each training example pairs an input prompt with a desired response. The model predicts the target tokens, a supervised loss measures the difference between its predictions and those targets, and training updates the weights to make the demonstrated continuation more likely in similar contexts.
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Prompt → target answer → supervised loss → weight update
For example, a dataset might pair a request for a three-item list with a correctly formatted three-item answer. Repeated examples can teach response formats, tone, instruction-following patterns, classification, or translation—provided suitable target responses can be written and curated.
What SFT is good at—and where it can fail
SFT is a natural fit when a good answer can be demonstrated directly. Its signal is concrete: the target response shows the model what to produce. But examples are not a guarantee of general competence. Narrow or low-quality data can teach brittle patterns, and training can overfit or memorize examples instead of generalizing to new cases.
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How RL-style fine-tuning changes behavior
The loop: prompt, sampled answer, score, policy update
In RL-style fine-tuning, the model samples one or more candidate responses to a prompt. A reward model, human preference signal, or programmable grader evaluates the response, and an optimization procedure updates the model’s policy—the probabilities with which it produces outputs—toward higher-reward behavior.
Prompt → sampled answer(s) → reward or grade → policy update
This is not simply random trial and error. The model’s generated responses are evaluated against a defined signal, and that feedback influences later output probabilities. A score might represent correctness, style, safety, or a task-specific metric. It is only useful to the extent that the evaluator captures what matters.
What RL is good at—and where it can fail
RL can be useful when response quality is easier to score than to express as one canonical target answer, or when performance depends on optimizing a measurable task objective. A grader can reward qualities that vary across valid answers rather than requiring a single exact wording.
The central risk is reward misspecification: a model may learn to score well without meeting the user’s real goal. An incomplete reward can steer behavior toward its blind spots, and improvements on a rewarded task can coincide with regressions elsewhere.
How the two methods compare
| Dimension | SFT | RL-style fine-tuning |
|---|---|---|
| Training signal | A desired target response for each example. | A reward, grader score, or preference signal for generated response(s). |
| Preparation | Curate representative prompt-and-target examples. | Curate prompts and build a reliable grader, reward model, or preference signal; sample outputs for scoring. |
| Update intuition | Increase the likelihood of target responses. | Shift the policy toward outputs that receive stronger reward, often with a policy-gradient method. |
| Good fit | Behavior that is straightforward to demonstrate, such as format, tone, instruction following, classification, or nuanced translation. | Behavior whose quality can be scored or tied to a task metric more readily than written as one target answer. |
| Main risk | Narrow or poor examples can cause brittle behavior or overfitting. | A faulty or incomplete reward can optimize the score rather than the user’s actual need, or cause regressions on other tasks. |
| Evaluation focus | Compare the fine-tuned model with the base model on held-out, representative examples. | Check both reward and real task performance, including cases and failure modes the grader may miss. |
These are tendencies, not guarantees. Practical systems may combine demonstrations, preference learning, and reward optimization.
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Can SFT and RL be used together?
Yes. OpenAI’s InstructGPT work provides a documented example of a staged pipeline, rather than a universal recipe:
- Train a supervised baseline: human writers create demonstrations, and the model is fine-tuned on those prompt-and-answer examples.
- Train a reward model: people compare model outputs, and those preference comparisons train a model to predict which responses are preferred.
- Optimize with RL: the policy is fine-tuned with Proximal Policy Optimization (PPO) against the learned reward model.
The project used human preferences because complex goals are not always captured by simple automatic metrics. OpenAI’s 2022 explanation characterized that specific procedure as using “less than 2% of the compute and data relative to model pretraining.” That figure describes the InstructGPT work relative to GPT-3 pretraining; it should not be generalized to other SFT or RL pipelines.
The same work reported an “alignment tax”: better customer-directed behavior came with reduced performance on some academic NLP tasks. In its experiments, mixing a small fraction of original pretraining data into RL fine-tuning was one mitigation. That is a historical result from one project, not a guaranteed fix for other models.
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Why evaluation matters as much as the training method
Neither a set of demonstrations nor a reward score proves that the model improved in ways users care about. SFT should be tested on held-out examples that represent real tasks. RL evaluation should examine both the grader’s score and the underlying task, including edge cases where a model might exploit a weakness in the grader.
OpenAI’s SFT documentation puts the ordering plainly: “Good evals first! Only invest in fine-tuning after setting up evals.” OpenAI’s supervised fine-tuning guide also discusses the role of examples, weight updates, and overfitting.
Results depend on the data, model, reward design, and evaluation setup. For example, a 2025 preprint studying an out-of-distribution variant of the 24-point card game reported that RL fine-tuning recovered some SFT-related performance loss in its experiments, but not all of it after severe SFT overfitting and distribution shift. Those findings are specific to the paper’s models and test setup, not a general benchmark result. The study, “RL Is Neither a Panacea Nor a Mirage,” is available as a 2025 preprint.
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Quick Recap
The practical mental model
- SFT: show the model target answers and update its weights to make those continuations more likely.
- RL: sample model answers, score them, and update the policy toward responses that earn stronger feedback.
- For both: the resulting behavior depends on what the data or evaluator rewards and how improvement is tested.
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