Agentic RFT: Reinforcement Fine-Tuning for AI Agents
How to build, evaluate, and improve agentic systems with trajectory-aware feedback.
Introduction
Agentic Reinforcement Fine-Tuning (RFT) is an approach to improving AI agents through reward-based feedback on real interaction trajectories—not just static data.
Instead of relying on fixed input-output pairs (as in traditional supervised fine-tuning), RFT teaches models from how they think, call tools, and solve tasks over multiple steps. You record entire "episodes" of behavior, grade them, and use those graded rollouts to guide learning.
This bridges reinforcement learning and fine-tuning: it's about using real reasoning traces and environment feedback to help an agent learn policies that work in production-like conditions.
Why RFT Matters
Conventional fine-tuning captures outputs, not reasoning. In agentic systems, decisions unfold across multiple steps—searching, parsing, calling APIs, and revising answers. RFT gives you a way to:
- Capture complete trajectories, not isolated outputs
- Evaluate performance in context, considering reasoning and tool use
- Simulate production conditions during training
- Provide graded (not binary) feedback to shape better policies
Table of Contents
1. How It Works: Key Components & Workflow
Agentic RFT uses trajectory logging and graded feedback loops. You can think of each agent run as a "mini-episode" that's stored, reviewed, and used for downstream learning.
1. Trajectory Tracking
Every agent session gets a unique ID that ties together the reasoning steps, tool calls, and final answer. This makes it easy to reconstruct and analyze the full context later.
2. Reasoning Phase
The model decomposes the problem and plans next actions—just like chain-of-thought prompting, but logged for evaluation.
3. Tool Calls & Outputs
When the model calls tools (search, database query, calculator, etc.), both the call and the result are stored alongside the trajectory. These calls are executed by your system—not the AI provider—to ensure reproducibility and safety.
4. Grader
After the task completes, a grader (which could be a model, human, or hybrid) reviews the full trajectory. It produces a reward signal, often scalar or categorical, representing task success, efficiency, or correctness.
5. Reward Model / Policy Update
The reward data is then used in one of several ways:
- Train a reward model (as in RLHF/RLAIF)
- Fine-tune via Direct Preference Optimization (DPO) or policy gradient methods
- Aggregate graded rollouts for offline imitation learning
6. Iteration
Repeat the loop with updated weights and new data. Over time, the agent's policy aligns more closely with desired multi-step behaviors.
2. Benefits of Agentic RFT
Full-Context Evaluation
Training data includes not just the "what" (output) but the "how" (reasoning and actions). This allows for nuanced rewards like efficiency, safety, or interpretability.
Environment Realism
The same infrastructure used for training can mirror your production runtime. While you can't achieve perfect replication (APIs drift, latency varies), close simulation improves transferability.
Flexible Reward Design
You can experiment with shaped rewards, partial credit, and multiple metrics—such as accuracy, number of tool calls, or cost per successful task.
Continuous Improvement
As your agent operates, each run becomes new training data. With careful logging and reward assignment, you can build a self-improving agent loop.
3. Example Use Case: FinQA-Style Task
Let's say you want to train a financial-reasoning agent similar to tasks from the FinQA dataset (Harvard & AI2).
The task: answer numerical questions based on financial reports.
Your setup might involve:
- A search tool to find relevant reports
- A load tool to open a file
- A calculator tool to perform arithmetic
Each full query–reason–tool–answer sequence is logged and graded. The grader might check correctness, reasoning trace quality, or adherence to numerical precision.
Important note
The original FinQA dataset doesn't contain tool calls—it's supervised QA data. Here, you're extending it into an agentic benchmark by wrapping it in tool-use scaffolding.
4. Getting Successful with Agentic RFT
1. Well-Defined Tasks
Start with tasks that have clear, objective outcomes (math, extraction, classification). Avoid fuzzy tasks like opinion generation until you have a robust reward model.
2. Baseline Competence
Your base model must already show non-zero skill. RFT amplifies existing strengths—it can't teach from pure noise.
3. Performance Metrics Beyond Accuracy
Track Accuracy@k, reasoning depth, or tool efficiency. These help you identify whether the agent "knows the right answer somewhere" even if it's not ranked first.
4. High-Quality Trajectories Over Volume
A few hundred well-graded episodes can be more valuable than thousands of noisy logs. Focus on diversity, completeness, and clean grading signals.
5. Infrastructure Alignment, Not Mirroring
Keep training and inference environments consistent at the interface level (tool schemas, APIs). Exact mirroring is impossible, but structural alignment matters most.
6. Invest in Your Grader
The grader is your teacher. It should:
- Capture domain-specific correctness
- Be resistant to reward hacking
- Produce continuous or preference-based signals, not just right/wrong
7. Practical Constraints
Avoid massive tool outputs; long responses can balloon training costs. Summarize or chunk large outputs for manageability.
Summary
Agentic RFT is not a new algorithm, but a practical engineering framework that combines:
- Trajectory logging (from production-like runs)
- Reward modeling or preference learning (grading trajectories)
- Policy improvement (PPO, DPO, or imitation)
Use it when your goal is to improve decision-making behavior, not just output formatting. Start small, measure carefully, and iterate.
Further Reading
- OpenAI: Reinforcement Learning from Human Feedback (RLHF)
- Anthropic: Constitutional AI
- Stanford DSPy: Programmatic Supervision and Self-Improving Pipelines
- DeepMind: Scalable Agent Alignment via Feedback Loops
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