Because tool calling has a naturally verifiable objective, whether the model called the right function with the right parameters, it maps well to RLVR. The challenge with self-managed reinforcement learning (RL) is the operational overhead. GPU procurement, memory orchestration between rollout and training phases, reward infrastructure, and checkpointing add up quickly. Hyperparameter sensitivity adds another layer of complexity. SageMaker AI takes on that work so you can focus on your model, your data, and your reward function.

SageMaker AI supports model families including Amazon Nova, GPT-OSS, Llama, Qwen, and DeepSeek, with techniques including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), RLVR, and Reinforcement Learning from AI Feedback (RLAIF). Training and validation metrics are tracked through integrated MLflow.

SFT requires labeled examples of each behavior that you want the model to learn. For tool calling, that means examples of calling a tool, asking for clarification, and refusing. But tool calling also requires the model to decide between those behaviors, and SFT can struggle to generalize that decision-making beyond the specific patterns in its training data.

RLVR works differently. For each prompt, the model generates multiple candidate responses (we use eight). A reward function verifies which ones are correct. The model then updates its policy to favor what worked, using Group Relative Policy Optimization (GRPO). GRPO compares each candidate’s reward score against the mean score of the group and reinforces responses that score above average. Over time, the model learns the format of a tool call and when to call compared to when to ask.