How to Improve Agent Performance with Fine‑Tuning: Key Strategies for AI Interviews

Interview Question: How to improve Agent performance via fine‑tuning?

Top agents such as Claude Code and ChatGPT Agent require dedicated fine‑tuning of the base model to handle domain‑specific tools and to stabilize performance when the base model cannot reliably recognize or invoke built‑in tools.

Standard Answer Overview

Two complementary approaches can be presented:

Efficient fine‑tuning

Construct a fine‑tuning dataset that reflects the concrete tasks the Agent performs. The dataset should contain:

Multi‑tool parallel call samples that teach the model to invoke several tools within a single dialogue turn.

Chain‑call tool samples that demonstrate reliable multi‑step tool sequences.

These samples enable the model to learn how to combine multiple built‑in tools and to execute stable, multi‑step tool workflows.

Reinforcement‑learning fine‑tuning

Beyond instruction fine‑tuning, reinforcement‑learning algorithms such as PPO, GRPO and GSPO can be applied. Reward functions typically combine three layers:

Correctness of tool invocation (parameters and order).

Task‑completion success (final answer matches the expected result).

Call‑chain efficiency (penalising redundant or ill‑ogical calls).

When an Agent completes a complex multi‑step task, the reward model assigns a higher score; failures or logical errors receive lower scores, guiding the model toward an optimal calling strategy.

Practical Demonstration

A concrete Function‑Calling dataset in ShareGPT format can be used with open‑source fine‑tuning frameworks such as LLamaFactory or Unsloth. Example JSON:

{
  "conversations": [
    {"from": "human", "value": "I saw a dress that I liked. It was originally priced at $200 but it's on sale for 20% off. Can you tell me how much it will cost after the discount?"},
    {"from": "function_call", "value": "{\"name\": \"calculate_discount\", \"arguments\": {\"original_price\": 200, \"discount_percentage\": 20}}"},
    {"from": "observation", "value": "{\"discounted_price\": 160}"},
    {"from": "gpt", "value": "The dress will cost you $160 after the 20% discount."}
  ],
  "tools": "[{\"name\": \"calculate_discount\", \"description\": \"Calculate the discounted price\", \"parameters\": {\"type\": \"object\", \"properties\": {\"original_price\": {\"type\": \"number\", \"description\": \"The original price of the item\"}, \"discount_percentage\": {\"type\": \"number\", \"description\": \"The percentage of discount\"}}, \"required\": [\"original_price\", \"discount_percentage\"]}}]"
}

For reinforcement‑learning fine‑tuning, the open‑source project SimpleGRPO from Fudan University can be referenced (GitHub: https://github.com/lsdefine/simple_GRPO/tree/main/Auto_Program).

Related Hot Questions

2.1 What is key to efficient fine‑tuning for Agent tool‑call capability?

Data quality and coverage are essential. Increasing dataset size alone is insufficient; training samples must span diverse tool‑call patterns in target scenarios and maintain diversity to avoid over‑fitting to a narrow behavior set.

2.2 How should reward functions be designed for RL fine‑tuning of Agents?

A hierarchical design is common: the first layer rewards correct tool invocation (parameters and order), the second layer rewards successful task completion (correct final answer), and the third layer rewards efficient call chains (minimal redundant calls and logical flow).

2.3 Why is RL more suitable than pure instruction fine‑tuning for improving Agents?

Instruction fine‑tuning only enables the model to imitate existing data, whereas RL lets the model self‑optimize in a dynamic environment. Agents often encounter highly uncertain situations that static datasets cannot fully cover; RL provides trial‑and‑error feedback that cultivates robust calling strategies.

Conclusion

Answering the interview question can be structured around (1) efficient fine‑tuning with multi‑tool parallel and chain‑call datasets, and (2) reinforcement‑learning fine‑tuning with layered reward functions and algorithms such as PPO, GRPO, or GSPO. Providing the concrete JSON example and citing the SimpleGRPO repository demonstrates concrete engineering experience.