Closes the rebuild plan's R8 scope: production runner shape for the
integrated RL trainer. Three artifacts wired end-to-end:
1. `crates/ml-alpha/examples/alpha_rl_train.rs` — clap CLI driving
`IntegratedTrainer::step_with_lobsim` against MBP-10 windows
loaded via `MultiHorizonLoader::next_sequence_pair` (R2) for
true `(s_t, s_{t+1})` adjacency. Per `feedback_mbp10_mandatory`,
`--mbp10-data-dir` is required — no synthetic-data fallback in
the production path.
2. `infra/k8s/argo/alpha-rl-template.yaml` — WorkflowTemplate
mirroring alpha-perception's DAG (check-cache → ensure-binary →
train; warmup-gpu parallel). Binary cache slot is
`/data/bin/<sha>/alpha_rl_train` (distinct from `alpha_train`
so the two binaries coexist at the same SHA).
3. `scripts/argo-alpha-rl.sh` — dispatcher with three rebuild-plan
guards baked in.
## Dispatcher guards (per the rebuild plan's feedback list)
`feedback_default_to_l40s_pool` (2026-05-09): default `--gpu-pool`
is `ci-training-l40s` (sm_89). H100 (sm_90) is opt-in for production
scale-up only. Cubins must match the device, so the dispatcher
derives `cuda-compute-cap` from the pool name and threads it into
the workflow params.
`feedback_argo_template_must_apply` (2026-05-21 canonical incident):
`argo submit --from=wftmpl/<name>` reads the cluster CRD, NOT the
on-disk YAML; unknown `-p` parameters silently no-op without a prior
`kubectl apply`. Dispatcher applies the local template BEFORE every
submission (overrideable via `--skip-template-apply` for the rare
case where you've already applied manually).
`feedback_push_before_deploy` (2026-05-20 canonical incident): the
in-cluster `ensure-binary` pod fetches source from `origin/<branch>`,
NOT the local working tree. Submitting before `git push` deploys the
last-pushed SHA, which can lag local diff by N commits. Dispatcher
verifies `git rev-parse HEAD == git rev-parse origin/<branch>` and
hard-errors with the explicit push command otherwise. Bypass via
`--skip-push-check` (only when intentionally deploying a previously-
pushed SHA via `--sha`).
## CLI: gate G8 (NaN abort)
Per `feedback_stop_on_anomaly` + `feedback_kill_runs_on_anomaly_quickly`,
the CLI checks every per-head loss (l_bce / l_q / l_pi / l_v / l_aux
/ l_total) for finiteness after each `step_with_lobsim` call.
Non-finite at any step → write summary with `nan_abort_step` set →
`process::exit(2)`. R9's cluster smoke tail-watcher kills the
workflow on the non-zero exit code, satisfying gate G8 from the
rebuild plan.
## CLI: knobs that ARE on the CLI
Structural / boundary parameters only (per
`pearl_controller_anchors_isv_driven`: every adaptive knob lives in
ISV, not CLI flags):
* `--mbp10-data-dir / --predecoded-dir / --out` — I/O paths.
* `--n-steps` — wall-budget control (1000 R9 smoke / 50k+ prod).
* `--seq-len / --n-backtests / --per-capacity` — structural
sizing. seq_len threads into the loader's multi-resolution
`1:<seq_len>` config; n_backtests into both LobSimCuda and
PerceptionTrainerConfig.n_batch.
* `--seed` — reproducibility per
`pearl_scoped_init_seed_for_reproducibility` (forks deterministic
sub-seeds for dqn / ppo / per).
* `--instrument-mode` — MBP-10 filter (all / front-month / id=N).
* `--gpu-idx` — CUDA device selection.
What's NOT on the CLI: γ / τ / ε / entropy_coef / per_α / reward_scale
/ per-head LRs — all live in ISV[400..417] and are driven by R5's
controllers from EMA-tracked diagnostics. Per the rebuild plan
A1: "every adaptive bound is signal-driven, not tuned."
## Cluster smoke entry point
```bash
# R9 validation smoke (after pre-cluster local CUDA tests green).
./scripts/argo-alpha-rl.sh --n-steps 1000 --instrument-mode front-month
# Production scale-up (gated by R9's multi-fold pass).
./scripts/argo-alpha-rl.sh --n-steps 50000 --n-backtests 32 \
--per-capacity 100000 # GPU sum-tree R-future when capacity > 4096
```
## What's NOT in this commit
The R9 cluster smoke run itself is out of band — this commit ships
the entry points. R9 will execute the pre-cluster validation
checklist + first 1000-step smoke + multi-fold walk-forward G8 gate
per the rebuild plan §"Cluster smoke discipline".
The summary JSON's schema is intentionally narrow (final-step losses
+ replay len + completion state + NaN abort marker). R-future may
add per-epoch breakdowns + per-ISV-slot snapshots once the cluster
smoke tells us which diagnostics are actually load-bearing for kill
decisions.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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