Supervised Fine-Tuning vs Reinforcement Learning (original) (raw)

Can large language models internalize decision rules that are never stated explicitly? To examine this, we designed an experiment in which a 14B parameter model was trained on a hidden “VIP override” rule within a credit decisioning task, without any prompt-level description of the rule itself.

Explore how supervised fine-tuning and reinforcement learning methods performed, their key differences, and our recommendations on choosing the most suitable method.

Benchmark results

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Using supervised fine-tuning, the model achieved 88% accuracy. In contrast, reinforcement learning with GRPO plateaued at 43%, only modestly above the 34% baseline.

These results highlight a key limitation of reward-only training signals when learning counterintuitive, rule-based behaviors. They also offer practical guidance on when supervised fine-tuning or reinforcement learning is the more appropriate choice.

What do these numbers mean?

We created a fictional company called FinCorp with its own proprietary credit decisioning rules. These rules differ from standard banking logic. We then tested whether different training methods could teach these rules to an LLM.

Key findings

Evaluation tasks

Task 1: FinCorp Credit Decision Classification

Task 2: Implicit Rule Learning (MANUAL_REVIEW Subset)

Why not just use a system prompt?

Two reasons:

  1. Security: Proprietary business logic should not appear in prompts.
  2. Complexity: Real companies may have dozens of rules that cannot reasonably fit in a prompt.

Fine-tuning embeds the rules directly into the model weights and avoids exposing them in the prompt.

Technical analysis and recommendations from our benchmark

Why RL failed: The credit assignment problem

Why RL showed mode collapse

Training logs indicate that the model converged to a narrow set of predictions that yielded occasional positive rewards. Exploration decreased, and the model failed to attempt the VIP logic at all.

When to use each method

This benchmark focuses on a case in which SFT has a structural advantage.

The hybrid approach

In practice, strong models often follow this sequence:

  1. SFT to teach the capability.
  2. RL to refine preferences and behavior.

This is the approach used in systems like ChatGPT and Claude.

What is supervised fine-tuning (SFT)?

Supervised fine-tuning is a post-training technique that adapts a pre-trained model to specific tasks using labeled datasets. In this process, the AI model is trained on input–output pairs where correct answers are explicitly provided. The goal is to shape model outputs so they align with task requirements, expected formats, and human expectations.

Supervised fine-tuning (SFT) is commonly applied to large language models after pretraining, making it a core part of the foundation model post-training.

For example, you provide input-output pairs, and the model learns to mimic them. Every token in the target output receives a direct gradient signal. The model knows precisely what it should have produced.

Input: “Founder Background: Ex-Google, Burn Rate: 93%…”

Output: {“decision”: “MANUAL_REVIEW”}

Think of it like teaching someone to cook by giving them a recipe with exact measurements. Follow the steps, and you get the dish.

Figure 1: The graph shows the pipeline in which a language model is first pre-trained on a large generic corpus, then supervised fine-tuned on labeled task-specific data to produce task-adapted models for applications such as summarization, classification, and text generation.1

Core characteristics

Common SFT variants

What is reinforcement learning (RL)?

Reinforcement learning is a paradigm in which an AI model learns optimal behaviors by interacting with an environment and receiving feedback in the form of rewards or penalties. Instead of labeled examples, the model improves by maximizing a reward function over time.

In artificial intelligence systems, reinforcement learning is widely used for dynamic environments and real-world scenarios where correct answers are not explicitly defined.

Model Output: {“decision”: “REJECT_RISK”}

Reward: -50 (Wrong)

Think of this like learning to cook by trial and error. You know the dish tastes bad, but you have to guess which ingredient caused the problem.

Figure 2: The graph shows the differences between online and offline learning, where agents learn policies by iteratively gathering data through direct interaction with an environment or by learning from previously logged data when direct interaction is impractical.2

Core characteristics

Supervised fine-tuning vs reinforcement learning: Key differences

Reinforcement learning and supervised fine-tuning are both post-training techniques for adapting a pre-trained model, but they solve fundamentally different problems. Understanding these differences is critical when choosing the right fine-tuning method for an AI system, especially for large language models and conversational AI.

At a high level, supervised fine-tuning teaches a model “what the correct answer” is, while reinforcement learning teaches a model “which behaviors lead to better outcomes over time”.

Learning signal and feedback mechanism

The most important distinction lies in how feedback is provided during the training process.

Key contrast:

Role of human input

Human involvement differs significantly between the two approaches:

This makes reinforcement learning particularly effective for aligning AI assistants with human expectations in areas such as conversational quality, tone, and reasoning models.

Scope of tasks and environments

Generalization

However, RL training is more unstable and sensitive to reward design, which is why SFT remains essential as a stabilizing step.

Training efficiency and complexity

From an operational perspective, supervised fine-tuning is more straightforward and more predictable. The training dataset is fixed, the evaluation metrics are clear, and the training efficiency is high when large labeled datasets are available.

Reinforcement learning is more complex and computationally expensive. Designing a practical reward function, managing exploration, and ensuring stable learning require careful tuning. Algorithms such as proximal policy optimization are often used to improve stability, but RL still demands more experimentation.

Position in modern AI training pipelines

In practice, reinforcement learning and supervised fine-tuning are not competitors but complementary techniques.

Most foundation model post-training pipelines follow a clear sequence:

  1. Start with a base model or foundation models
  2. Apply supervised fine-tuning SFT to stabilize model outputs
  3. Use subsequent RL to align behavior with human preferences

SFT provides a solid foundation by teaching correctness and format. RL then refines behavior, improving model performance in areas where correctness alone is insufficient.

Emerging products

verl: Volcano Engine Reinforcement Learning for LLMs

verl (Volcano Engine Reinforcement Learning for LLMs) is an open-source framework developed by the ByteDance Seed team for reinforcement learning–based post-training of large language models (LLMs), including:

The framework focuses on enabling efficient implementation of reinforcement learning algorithms such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO) for LLM training. It provides infrastructure to manage the key stages of reinforcement learning for language models, including response generation, reward computation, advantage estimation, and policy updates.

Architecture and operational principles

Reinforcement learning pipeline for LLMs

In reinforcement learning–based LLM training, a model generates outputs for given prompts and receives feedback through a reward signal. The training objective is to adjust the model parameters so that responses with higher rewards become more likely.

The general pipeline supported by verl includes the following stages:

  1. Prompt sampling: Prompts are drawn from a dataset used for reinforcement learning training.
  2. Response generation: The policy model (the LLM being optimized) generates responses for the prompts.
  3. Reward evaluation: A reward model or evaluation function assigns a reward score to each generated response. This reward may come from:
    • a learned reward model
    • rule-based scoring
    • automated evaluation systems.
  4. Advantage estimation: Reinforcement learning signals such as advantages or returns are computed based on the reward.
  5. Policy optimization: The policy model parameters are updated using an RL algorithm (e.g., PPO or GRPO).
  6. Iteration of the training loop: The process repeats until convergence or completion of the training schedule.

verl coordinates these components and manages their execution across distributed compute resources.3

OpenRLHF

OpenRLHF is an open-source framework aims to provide a scalable, high-performance, and accessible system for RL-based LLM alignment and optimization.

System architecture

Ray-based distributed architecture

OpenRLHF introduces a Ray-based RLHF architecture that manages distributed training across GPU clusters. Ray functions as the central scheduling and orchestration layer, coordinating resource allocation, task execution, and communication among different components.

The architecture separates system responsibilities into distinct roles:

Reinforcement learning training workflow

OpenRLHF implements a PPO-based RLHF training loop consisting of four main stages:

  1. Rollout generation: The policy model generates responses to input prompts using a rollout engine powered by vLLM.
  2. Reward computation: A reward model evaluates generated responses and assigns scalar rewards.
  3. Advantage estimation: Advantages are computed using Generalized Advantage Estimation (GAE), incorporating KL penalties to limit divergence from a reference policy.
  4. Policy optimization: Model parameters are updated using PPO’s clipped objective function.

Figure 3: Diagram showing OpenRLHF’s PPO workflow.4

Distributed system design

OpenRLHF incorporates several architectural features that enable efficient large-scale RLHF training.

1. 3D parallelism

The framework employs a three-dimensional parallelization strategy that combines:

This strategy is implemented using DeepSpeed ZeRO and ring attention mechanisms. Ring attention distributes attention computation across GPUs using a ring communication topology, which improves scalability for long-context reasoning tasks.

2. Accelerated inference with vLLM

Because inference dominates RLHF training time, OpenRLHF integrates vLLM to accelerate response generation. vLLM provides several optimizations:

These techniques improve GPU utilization and significantly increase inference throughput during RLHF training.

3. Asynchronous dataflow

OpenRLHF supports asynchronous execution between system components, including rollout engines and training engines.

Rather than waiting for all processes to complete before proceeding, each component operates independently and communicates through message passing. This asynchronous design prevents slow tasks, such as long Chain-of-Thought generations, from blocking the entire training pipeline.

As a result, system throughput and hardware utilization improve significantly in distributed environments.

Performance evaluation

Experimental results demonstrate that OpenRLHF achieves significant performance improvements over existing RLHF frameworks. Key findings include:

These improvements are primarily attributed to:

Methodology

We ran all experiments on a single NVIDIA A100 (80GB) using PyTorch 2.x, HuggingFace Transformers, and TRL 0.27.0. All training used LoRA adapters (r=16, α=32) applied to the query, key, value, and output projections, with bfloat16 precision.

The base model was Qwen3-14B-Instruct for all three conditions: baseline (no fine-tuning), RL (GRPO with LoRA), and SFT (with LoRA).

For the dataset, we generated 800 synthetic loan applications with balanced class distribution (200 per class), split 80/20 into training (640 samples) and test (160 samples) sets.

How the credit decisioning system works

The core mechanism: We built a synthetic credit decisioning system with four possible outcomes and a strict priority hierarchy:

DECISION HIERARCHY (Priority Order)

1. MANUAL_REVIEW (Founder is Ex-Google or Ex-Facebook, hidden rule)

2. REJECT_RISK (Revenue > $10M and Burn Rate > 80% of Revenue)

3. A_PLUS_TIER (Customer NPS Score ≥ 80)

4. STANDARD_LOAN (Default case)

The critical test is that Rule 1 is never mentioned in the system prompt. The model must discover it purely from training signals.

Where it breaks down:

The VIP override rule is intentionally counterintuitive. A founder with poor financial metrics but a background at Google should receive MANUAL_REVIEW, even though financial reasoning alone would produce REJECT_RISK.

Limitations

This is an exploratory study intended to provide directional insights for practitioners evaluating SFT vs RL trade-offs. These findings should inform your own experiments, not serve as universal conclusions.

Experimental scope:

RL was not given equal conditions:

Task design favored SFT:

Future work

For future work, we aim to extend this benchmark along several dimensions:

Conclusion

This experiment shows that Supervised Fine-Tuning significantly outperforms Reinforcement Learning for explicit and rule-based behaviors, especially when those rules contradict typical reasoning patterns. SFT learned the hidden VIP override rule with 86% accuracy, whereas RL missed it almost entirely at 7%.

From what we have learned from this benchmark, here are some practical recommendations:

  1. Use SFT whenever you can provide labeled examples.
  2. Use RL for subjective optimization rather than capability learning.
  3. Combine SFT and RL when you need both precision and preference alignment.

The broader lesson is straightforward: whenever direct supervision is possible, use it.

Cite this research

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Ekrem Sarı and Sıla Ermut (2026) - "Supervised Fine-Tuning vs Reinforcement Learning". Published online at AIMultiple.com. Retrieved March 5, 2026, from: https://aimultiple.com/rl-vs-sft [Online Resource]

Sarı, E., & Ermut, S. (2026, March 5). Supervised Fine-Tuning vs Reinforcement Learning. AIMultiple. https://aimultiple.com/rl-vs-sft

@misc{sar2026, author = {Sarı, Ekrem and Ermut, Sıla}, title = {{Supervised Fine-Tuning vs Reinforcement Learning}}, year = {2026}, month = mar, howpublished = {\url{https://aimultiple.com/rl-vs-sft}}, note = {AIMultiple. Retrieved March 5, 2026} }

Ekrem Sarı

Ekrem is an AI Researcher at AIMultiple, focusing on intelligent automation, GPUs, AI Agents, and RAG frameworks.

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Researched by

Sıla Ermut

Sıla Ermut

Industry Analyst

Sıla Ermut is an industry analyst at AIMultiple focused on email marketing and sales videos. She previously worked as a recruiter in project management and consulting firms. Sıla holds a Master of Science degree in Social Psychology and a Bachelor of Arts degree in International Relations.

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