For years, supervised fine-tuning (SFT) has been the go-to method for customizing large language models (LLMs). However, a new approach called Reinforcement Fine-Tuning (RFT) is emerging as a powerful alternative, especially when labeled data is limited. This article breaks down the key differences between RFT and SFT, and provides a framework for deciding which method is best for your specific use case.

What is Supervised Fine-Tuning (SFT)?

Supervised Fine-Tuning (SFT), a process where a pre-trained model is further refined using labeled and curated datasets to improve its performance for specific tasks, requires human-created datasets with precise labels, ensuring the model learns patterns correctly.

Considered essential for fine-tuning AI to perform specialized tasks, the cost and effort involved in SFT make data efficiency strategies and automation in labeling critical for scalable AI adoption.

What is Reinforcement Fine-Tuning (RFT)?

RFT applies reinforcement learning techniques to fine-tune language models, optimizing them for specific tasks and domains. Unlike SFT, which relies on labeled prompt-completion pairs, RFT uses a reward function to score the correctness of generated outputs. This allows the model to learn and improve through trial and error, even without explicit labels.

Key Differences Between RFT and SFT

Supervised Fine-Tuning (SFT) relies on labeled data and provides structured learning but may suffer from overfitting with limited datasets. Reinforcement Fine-Tuning (RFT), on the other hand, uses a reward-based approach, making it more adaptable and less dependent on labeled data. While SFT ensures consistency, RFT offers greater flexibility and resilience to overfitting, especially in dynamic scenarios.

When to Choose RFT Over SFT

• • Labeled data is scarce: As a rule of thumb, if you have fewer than 100 labeled examples, RFT may be a better choice.
  • You can verify output correctness: Even without labels, RFT can work if you have a way to automatically determine whether the generated output is correct. This could involve using a code interpreter, a solver, or a game engine.
  • Chain-of-thought reasoning is beneficial: If your task benefits from step-by-step logical reasoning, RFT can help improve the model’s reasoning abilities.

“Reinforcement fine-tuning allows models to evolve dynamically, learning from feedback rather than static datasets. This makes it particularly powerful for optimizing responses based on real-world interactions.”

Reinforcement Fine-Tuning (RFT) & DeepSeek’s Success

DeepSeek’s rise highlights the power of Reinforcement Fine-Tuning (RFT). Unlike Supervised Fine-Tuning (SFT), RFT trains models by reinforcing desirable outputs, reducing the need for extensive manual annotation. Instead of labels, RFT relies on verifiers/validators to assess and refine responses, making the process more scalable.

DeepSeek also leveraged LoRA (Low-Rank Adaptation), a technique that fine-tunes models efficiently by modifying only a small subset of parameters. This approach enables cost-effective AI adaptation without retraining entire models.

RFT Shines in Specific Scenarios

• • Medical Advice – Fine-tunes models for accurate, domain-specific medical guidance using limited labeled data.
  • Creative Content – Trains models to generate poetry, stories, and more using a grader for originality.
  • Math Problem-Solving – Reinforces correct reasoning and answers for better generalization.
  • Domain-Specific Q&A – Adapts models to answer specialized questions in fields like science or history.
  • Code Generation & Debugging – Rewards correct, efficient code that compiles and meets requirements.

RFT Improves Reasoning with Chain-of-Thought

RFT can significantly enhance reasoning strategies when using chain-of-thought prompting. Unlike SFT, which primarily distills reasoning from teacher models, RFT enables models to discover and refine novel reasoning approaches that maximize correctness. This makes RFT particularly effective for tasks requiring structured reasoning, logic-based decision-making, and mathematical problem-solving.

Algorithms Used in RFT

• • Deep Deterministic Policy Gradient (DDPG) – Handles continuous action spaces using an off-policy actor-critic approach, ideal for precise output control.
  • Soft Actor-Critic (SAC) – Maximizes cumulative rewards while encouraging exploration, ensuring stable learning in complex tasks.
  • Trust Region Policy Optimization (TRPO) – Ensures stable policy updates by constraining changes within a trust region, improving reliability.
  • Actor-Critic with Experience Replay (ACER) – Enhances learning efficiency by combining actor-critic methods with experience replay.
  • Reinforcement Learning from Human Feedback (RLHF) – Uses human feedback as rewards to fine-tune AI for better alignment with human preferences.

A Heuristic Process for Choosing a Fine-Tuning Method

• • If your task is based on subjective human preferences, like creative writing, use Reinforcement Learning from Human Feedback (RLHF).
  • If your task has objectively correct answers, use either RFT or SFT.
  • If you have a lot of high-quality labeled data and reasoning isn’t critical, use SFT.
  • If labeled data is scarce, you can verify correctness, or reasoning is important, use RFT.

Getting Started with RFT

RFT is a rapidly evolving field, and it offers exciting possibilities for customizing LLMs with limited data. To delve deeper and explore practical applications, consider:

• • Exploring platforms that offer tools and support for RFT and SFT.
  • Experimenting with different RFT algorithms and reward functions.
  • Sharing your findings and contributing to the growing RFT community.

Striking the Right Balance between SFT and RFT

Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT) each offer unique advantages in refining large language models. While SFT ensures structured learning and factual accuracy, RFT enhances adaptability and responsiveness. The optimal approach depends on the application. SFT excels in knowledge-driven tasks, whereas RFT is better suited for interactive and evolving environments.

“Neither approach alone is sufficient. SFT provides reliability, while RFT enhances adaptability. The future lies in hybrid methodologies that leverage the best of both worlds.”

As AI continues to advance, a hybrid strategy that combines the precision of SFT with the flexibility of RFT will drive the next generation of intelligent systems. Striking the right balance between the two will be key to unlocking more capable, context-aware, and user-centric language models.

How Fusefy Can Help with RFT Implementation

• • Arch Engine – Helps design RFT models tailored to specific AI applications, optimizing architecture selection and training strategies.
  • ROI Intelligence – Evaluates the cost-benefit ratio of RFT adoption, ensuring efficiency in fine-tuning models without unnecessary expenses.
  • Datasense – Analyzes data requirements, reducing reliance on large labeled datasets by identifying the best feedback mechanisms for reinforcement learning.

To explore more on DeepSeek’s capabilities, read our blog on AI Cost Revolution: DeepSeek’s Impact & Fusefy’s Strategy – Welcome to Fusefy For Pragmatic AI

With over a decade of experience, Sindhiya Selvaraj is the Chief Architect at Fusefy, leading the design of secure, scalable AI systems grounded in governance, ethics, and regulatory compliance.