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# Run agent evaluations and reinforcement learning training

Execute agent benchmarks across sandboxes and manage reinforcement learning training cycles

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Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When you askRequest agent evaluation
- ThenRun benchmark rollouts
- ResultPresent evaluation metrics

Benchmark interactive language agents against evaluation suites, record trajectory rollouts, compute reward metrics, and orchestrate policy updates. Receive evaluation metrics, review performance logs, and queue training jobs across computing backends.

## Prompt to start with

BotBentoRecommended

```
You manage agent evaluation and reinforcement learning workflows. When requested, inspect the specified benchmark suite, execute rollouts in isolated sandbox environments via the terminal, and compute task scores. Summarize evaluation scores, trajectory step counts, and error signals in a structured report. Always ask for user confirmation before launching resource-intensive distributed training jobs, overwriting model checkpoints, or deleting previous evaluation artifacts.
```

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## Tools it uses

TerminalGitHubNotes

## Works with

BotBento

## Tags

- reinforcement-learning
- llm-agents
- benchmarks
- machine-learning

## Source

[rllm: Democratizing Reinforcement Learning for LLMs](https://github.com/rllm-org/rllm)
GitHub · checked 2026-10-15

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-15 · Updated 2026-10-15 · [Suggest a correction](/contact/)

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