> ## 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.

# Single-Prompt Multi-Generation

> One rollout request, N outputs — engine-side conditioning expansion, microgroup mechanics, and seed layout.

GRPO needs `n_samples_per_prompt` outputs per prompt to normalize advantages within a group. The naive way is N separate
requests, which encodes the same prompt N times and denoises N batches of size 1. Miles-diffusion instead sends **one
request per microgroup**: sglang-diffusion encodes the conditioning once, expands it engine-side, and denoises all N
outputs as a batch. Engine-side expansion is opt-in per model family in sglang-diffusion.

## 1. Microgroups

`--rollout-microgroup-size M` splits each prompt group of `n_samples_per_prompt` samples into requests of at most M
(`generate_and_rm_group` in `miles/rollout/sglang_diffusion_rollout.py`). Each request carries
`num_outputs_per_prompt = M`:

```
group (1 prompt × n_samples_per_prompt samples)
  ├─ microgroup 0  → POST /rollout/generate  num_outputs_per_prompt=M
  ├─ microgroup 1  → POST /rollout/generate  num_outputs_per_prompt=M
  └─ ...
```

Microgroups of one group run as concurrent asyncio tasks, load-balanced across engines; in-flight requests per engine
are bounded by `--sglang-server-concurrency`.

Canonical values on `main`:

| Recipe                      | `n_samples_per_prompt` | microgroup size                              |
| --------------------------- | ---------------------- | -------------------------------------------- |
| SD3.5 GRPO + OCR            | 16                     | 8                                            |
| Qwen-Image GRPO + PickScore | 16                     | 8                                            |
| Wan2.2 GRPO + PickScore     | 16                     | 8                                            |
| LTX-2.3 GRPO + PickScore    | 8                      | 1 — multi-gen works but single-gen is faster |
| Cosmos3 GRPO                | 16                     | 1 — packed forward is single-sample          |

LTX-2.3 supports engine-side expansion, but the recipe generates one output per request: a multi-output video response
is large enough that deserializing and scoring it stops overlapping with other in-flight requests, so single-output
requests pipeline better end to end.

## 2. Seed layout

Rollout stays deterministic and collision-free per sample:

* sgl-d expands one request's `seed` into `seed, seed+1, …, seed+M−1` — one RNG stream per output.
* The trainer spaces request seeds so streams never overlap:

```python theme={null}
seed_base = (rollout_seed + group_index * n_samples_per_prompt) % 2**31
microgroup_seed = seed_base + idx   # idx = offset of the microgroup in its group
```

`group_index` is monotonic across the run, so every `(rollout, prompt-group, sample)` triple gets a distinct seed. This
is what makes rollout replay and [deterministic mode](/docs/diffusion/advanced/deterministic) possible at microgroup granularity.

## 3. Pairs well with

* [Streaming Reward and Deserialization](/docs/diffusion/advanced/streaming-reward) — what happens to a microgroup response after
  generation.
* [Deterministic Training](/docs/diffusion/advanced/deterministic) — seed layout is half of run reproducibility.
* [Core Concepts](/docs/diffusion/user-guide/concepts) — where microgroup size sits among the batch knobs.