> ## Documentation Index
> Fetch the complete documentation index at: https://miles.radixark.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Retool v2

> Tool-enabled language model generation with sandboxed Python code execution interleaved with thinking.

This example is an upgraded version of the original retool example, using the updated interfaces provided by the miles framework to implement multi-turn RL training with tool calls in a cleaner way.

## Key Differences from v1

**v1 (retool)** requires manually implementing the full multi-turn conversation loop in `generate_with_retool.py`, directly depending on low-level `GenerateState` and `sglang_rollout` interfaces — resulting in verbose code tightly coupled to the framework internals.

**v2 (retool\_v2)** uses the framework's standard plugin interfaces. Users only need to implement three functions and mount them via command-line arguments:

| Argument                                | Description                                                                                                            |
| --------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
| `--custom-generate-function-path`       | Uses the built-in `miles.rollout.generate_hub.multi_turn.generate` — no need to implement the multi-turn loop yourself |
| `--generate-tool-specs-path`            | Declare tool definitions (user-implemented)                                                                            |
| `--generate-execute-tool-function-path` | Implement tool execution logic (user-implemented)                                                                      |
| `--custom-rm-path`                      | Implement the reward function (user-implemented)                                                                       |

Users only need to focus on business logic (tool definitions, tool execution, reward calculation). Multi-turn scheduling, token concatenation, loss masking, etc. are all handled by the framework.

## Files

* `tool_sandbox.py`: Tool definitions (`tool_specs`), tool execution (`execute_tool`), reward function (`reward_func`), and sandboxed safe execution environment
* `run_retool_multi_turn.py`: Training launch script

## Quick Start

```bash theme={null}
python examples/retool_v2/run_retool_multi_turn.py
```

The launch script prepares everything it needs on its own: it downloads the dapo-math-17k
training set and the aime-2024 eval set, downloads the checkpoint, and converts it to
`torch_dist` before training starts.