@@ -124,9 +124,9 @@ export PRIME_API_KEY=...
 We currently support most major clients (OpenAI, Anthropic), as well as the router platforms (OpenRouter, Portkey). For local models, we recommend using vLLM (which interfaces with the [OpenAI client](https://github.com/alexzhang13/rlm/blob/main/rlm/clients/openai.py)). To view or add support for more clients, start by looking at [`rlm/clients/`](https://github.com/alexzhang13/rlm/tree/main/rlm/clients).
 
 ## Training
-You can RL-train policies directly against this repo using the [`training/`](https://github.com/alexzhang13/rlm/tree/main/training) harness, which exposes `rlm.RLM` as a [`verifiers`](https://github.com/willccbb/verifiers) `Environment` and plugs straight into [`prime-rl`](https://github.com/PrimeIntellect-ai/prime-rl). See the [training README](https://github.com/alexzhang13/rlm/tree/main/training#readme) for the launch command. The harness uses subprocess-isolated local REPL execution (no cloud sandboxes), matching the `local` environment above.
+We provide a simple RL training harness for training RLMs used in this repo (specifically the `local` REPL). The implementation uses no sandboxes for simplicity and slots easily your use case, but an ideal setup would use sandboxes for safety. Training logic is isolated to the [`training/`](https://github.com/alexzhang13/rlm/tree/main/training) folder, which exposes `rlm.RLM` as a [`verifiers`](https://github.com/willccbb/verifiers) `Environment` and plugs straight into [`prime-rl`](https://github.com/PrimeIntellect-ai/prime-rl). See the [training README](https://github.com/alexzhang13/rlm/tree/main/training#readme) for the launch command. The harness uses subprocess-isolated local REPL execution (no cloud sandboxes), matching the `local` environment above.
 
-A worked example lives in [`training/environments/oolong/`](https://github.com/alexzhang13/rlm/tree/main/training/environments/oolong) (OOLONG long-context QA). New training environments can be added the same way — author a `verifiers` env that wraps your task (see the [verifiers docs](https://verifiers.readthedocs.io/)), then reference it from a config.
+A worked example with an example `.toml` lives in [`training/environments/oolong/`](https://github.com/alexzhang13/rlm/tree/main/training/environments/oolong) (OOLONG long-context QA). New training environments can be added the same way — author a `verifiers` env that wraps your task (see the [verifiers docs](https://verifiers.readthedocs.io/)), then reference it from a config.
 
 ## Relevant Reading
 * **[Dec '25]** [Recursive Language Models arXiv](https://arxiv.org/abs/2512.24601)