clean up fully async example and mv to examples/infra_features

docs/docs.json

@@ -259,7 +259,6 @@
        {
         "group": "Recipes",
         "pages": [
-         "examples/fully-async",
          "examples/geo3k-vlm",
          "examples/geo3k-vlm/multi-turn",
          "examples/multi-lora",
@@ -275,6 +274,7 @@
         "group": "Infra Features",
         "root": "examples/infra-features",
         "pages": [
+         "examples/infra-features/fully-async",
          "examples/infra-features/low-precision",
          "examples/infra-features/p2p-weight-transfer",
          "examples/infra-features/random-async",

docs/models/glm/glm5-2.md

@@ -146,4 +146,4 @@ The launcher exposes these as flags:
 - [Low Precision RL](/advanced/low-precision) — opt-in via `--fp8-rollout`.
 - [Speculative Decoding](/advanced/speculative-decoding) — opt-in via `--enable-mtp`.
 - [LoRA](/advanced/lora) — via `scripts/run_glm5_2_744b_a40b_lora.py`.
-- [Fully Async Rollout](/examples/fully-async) — the terminal-bench-2 agentic example (§4.2) runs fully async.
+- [Fully Async Rollout](/examples/infra-features/fully-async) — the terminal-bench-2 agentic example (§4.2) runs fully async.

docs/user-guide/fully-async.md

@@ -35,9 +35,8 @@ Four launch scripts show the mode end to end, from a single-node smoke test to a
 
 | Script | What it covers |
 |---|---|
-| [`run-qwen3-4b-fully_async.sh`](https://github.com/radixark/miles/blob/main/examples/fully_async/run-qwen3-4b-fully_async.sh) | The smallest complete run: Qwen3-4B on one engine per GPU, with `--max-weight-staleness` shown as a commented-out option |
-| [`run_qwen3_30b_a3b_fully_async.py`](https://github.com/radixark/miles/blob/main/examples/fully_async/run_qwen3_30b_a3b_fully_async.py) | The same pattern on a 30B MoE, with `tp=8`, `ep=8`, and one 8-GPU rollout engine |
-| [`run_qwen3_5_4b_fully_async_eval.py`](https://github.com/radixark/miles/blob/main/examples/fully_async/run_qwen3_5_4b_fully_async_eval.py) | Both checkpoint eval backends behind one flag, `--eval-backend fleet` or `--eval-backend external` |
+| [`run_qwen3_30b_a3b_fully_async.py`](https://github.com/radixark/miles/blob/main/examples/infra_features/fully_async/run_qwen3_30b_a3b_fully_async.py) | The same pattern on a 30B MoE, with `tp=8`, `ep=8`, and one 8-GPU rollout engine |
+| [`run_qwen3_5_4b_fully_async_eval.py`](https://github.com/radixark/miles/blob/main/examples/infra_features/fully_async/run_qwen3_5_4b_fully_async_eval.py) | Both checkpoint eval backends behind one flag, `--eval-backend fleet` or `--eval-backend external` |
 | [`run_glm5_2_744b_a40b_daytona.py`](https://github.com/radixark/miles/blob/main/examples/experimental/openenv/glm52_tbench2/run_glm5_2_744b_a40b_daytona.py) | GLM-5.2 744B-A40B on 16 GB300 nodes, split 8 training and 8 inference, with multi-turn terminal-bench-2 episodes in per-task Daytona sandboxes. It runs 128 in-flight trajectories against a 64-sample train batch and evaluates on the shared rollout engines |
 
 ### Customizations
@@ -247,7 +246,7 @@ them explicitly with `--eval-sglang-*` if the fleet is large enough to want them
 ### Mode 3: External backend
 
 The contract lives in [`miles/rollout/checkpoint_eval.py`](https://github.com/radixark/miles/blob/main/miles/rollout/checkpoint_eval.py), with a
-reference implementation in [`examples/fully_async/external_eval_fn.py`](https://github.com/radixark/miles/blob/main/examples/fully_async/external_eval_fn.py).
+reference implementation in [`examples/infra_features/fully_async/external_eval_fn.py`](https://github.com/radixark/miles/blob/main/examples/infra_features/fully_async/external_eval_fn.py).
 
 Subclass `CheckpointEvalFn` and implement `evaluate_checkpoint(checkpoint_dir, input)`.
 The trainer hands over a snapshot path per eval point and owns dispatch, logging, and

examples/README.md

@@ -6,7 +6,6 @@ These examples are runnable starting points for your own RL workflow. A few are
 
 End-to-end training workflows — the place to start.
 
-- **[fully_async](./fully_async)**: Demonstrates fully asynchronous rollout generation for higher efficiency.
 - **[geo3k_vlm](./geo3k_vlm)**: Training VLMs with FSDP using GRPO on the GEO3K dataset.
   - **[multi_turn](./geo3k_vlm/multi_turn)**: The same dataset over multiple turns, with the model cropping images through an interactive environment.
 - **[lora](./lora)**: LoRA fine-tuning with the Megatron backend.
@@ -22,6 +21,7 @@ End-to-end training workflows — the place to start.
 Runtime and infrastructure plumbing rather than training recipes — how miles moves
 data and weights around.
 
+- **[fully_async](./infra_features/fully_async)**: Demonstrates fully asynchronous rollout generation for higher efficiency.
 - **[low_precision](./infra_features/low_precision)**: Examples of FP8 training and inference, plus INT4 QAT, for improved throughput and stability.
 - **[p2p_weight_transfer](./infra_features/p2p_weight_transfer)**: Point-to-point weight transfer between training and rollout engines.
 - **[random_async](./infra_features/random_async)**: Dataset-free stress test of the async rollout ↔ trainer loop.