Core Concepts

The four objects that make up every Miles job, and how data flows between them.

A Miles training job is a loop over four objects. Once you understand what each one is and how data flows between them, every flag in the system has an obvious home.

The four objects

Object	Role	Lives in
Prompt dataset	Source of input examples	JSONL on disk (or --data-source-path)
Rollout (SGLang engines)	Generates responses given prompts	One or more SGLang servers behind a router
Reward model	Maps (prompt, response, label) → score	Built-in (--rm-type) or custom (--custom-rm-path)
Actor (Megatron / FSDP)	The model being trained	Megatron torch_dist checkpoint, or HF directory under FSDP
Reference	Frozen copy of the actor for KL anchoring	Loaded from --ref-load, never updated

The training loop

for it in range(num_rollout):
    # 1. Sample
    prompts   = dataset.sample(rollout_batch_size)
    responses = sglang.generate(prompts, n=n_samples_per_prompt)

    # 2. Score
    rewards   = reward_fn(prompts, responses, labels)

    # 3. Optimize
    for step in range(num_steps_per_rollout):
        batch = pack(prompts, responses, rewards, size=global_batch_size)
        loss  = grpo_loss(actor, ref_model, batch)
        loss.backward(); optimizer.step()

    # 4. Sync
    p2p_weight_transfer(actor → sglang_engines)

That’s the whole thing. Every flag in Miles configures one of these four steps.

The four-knob invariant

Two knobs govern the sampling half of the loop, two govern the training half, and they are locked into a single equation:

rollout_batch_size × n_samples_per_prompt
  = global_batch_size × num_steps_per_rollout

Every sample produced by rollout is consumed by training, and every sample consumed by training was produced by rollout. Set any three sides; Miles fills in the fourth. Set all four inconsistently and Miles aborts with a validation error.

Where every flag goes

Use this map when reading any launch script:

Argument group	Concerns
model args	Architecture constants (layers, hidden size, rotary base, …)
ckpt_args	Filesystem paths for the actor / reference / save directory
rollout_args	Prompt dataset, batch knobs, sampling parameters, reward type
eval_args	Eval dataset, cadence, sampling overrides for evaluation
perf_args	Parallelism (TP/PP/CP/EP/ETP), recomputation, dynamic batching
grpo_args	RL algorithm, KL, clipping, entropy bonus, advantage estimator
optimizer_args	Learning rate, schedule, weight decay, Adam betas
sglang_args	Engine TP, memory fraction, log level, --sglang-* passthrough
misc_args	GPU layout, colocation, pinned Megatron numerics, dashboard

Next

• Launch Script — what a launch script does when you run it, and how to override a recipe.
• Training Backends — Megatron-LM and FSDP, parallelism, checkpoints, and hooks.
• Argument Groups — where each launch-script array belongs.
• CLI Reference — every flag, grouped and fully cataloged.