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README.md

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@@ -8,12 +8,12 @@ RL environment abstraction for [Strands Agents](https://github.com/strands-agent
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## Features
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This package standardizes agent environments by treating each `env.step()` as a full agent loop (`prompt → (tool_call, tool_response)* → response`), not a single model call. Built on [strands](https://github.com/strands-agents/sdk-python) agent loop and [`strands-sglang`](https://github.com/horizon-rl/strands-sglang) for RL training.
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This package standardizes agent environments by treating each `env.step()` as a full agent loop (prompt → (tool_call, tool_response)* → response), not a single model call or tool call. Built on [strands](https://github.com/strands-agents/sdk-python) agent loop and [`strands-sglang`](https://github.com/horizon-rl/strands-sglang) for RL training.
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- **Define environments easily** — subclass `Environment` and implement tools as `@tool` functions
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- **Capture token-level observations** — token-in/token-out trajectories for on-policy RL training (SGLang backend)
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- **Plug in reward functions** — evaluate agent outputs with custom `RewardFunction`
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- **Run benchmarks**`Evaluator` with pass@k metrics, checkpointing, and resume
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- **Run benchmarks**`Evaluator` with flexible environment setup, metric customization, and resume
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## Install
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