fix(build): cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP for cubin arch

Without this trigger, cargo treated CUDA_COMPUTE_CAP-driven cubin
compilation as cached output of a non-tracked env var. The cargo-target
PVC is shared across H100 (sm_90) and L40S (sm_89) training jobs, so
swapping --gpu-pool between archs left stale cubins from the previous
build cached for any future sm_X variation.

Symptom (T10 train-multi-seed-rn559 today):
  Failed to create DQN: rmsnorm cubin load: DriverError(
    CUDA_ERROR_NO_BINARY_FOR_GPU,
    "no kernel image is available for execution on the device")

The training pod scheduled on L40S (sm_89), the binary cache served
sm_90 cubins from a prior H100 build, and the rmsnorm cubin had no
sm_89 entrypoint.

Fix is one line: tell cargo to invalidate when CUDA_COMPUTE_CAP changes.
First post-fix build will rebuild all cubins (cargo conservatively
reruns when a new rerun-if-env-changed trigger is added without prior
state). Subsequent builds rebuild only on actual env changes.

Generalises the same lesson as the recent SP7 host-branch-in-captured-graph
fix: env-var-conditional code that doesn't declare its dependencies
freezes at first observation regardless of runtime input.

crates/ml/build.rs

@@ -36,7 +36,12 @@ fn main() {
     println!("cargo:rerun-if-changed={}", trade_physics_header.display());
     println!("cargo:rerun-if-changed={}", sp4_histogram_p99_header.display());
 
-    // Detect GPU architecture from env or default to sm_80
+    // Detect GPU architecture from env or default to sm_80.
+    // `rerun-if-env-changed` is mandatory: the cargo-target PVC is shared
+    // across H100 (sm_90) and L40S (sm_89) jobs, and without this trigger
+    // cargo serves stale cubins compiled for the previous arch on every
+    // subsequent build, producing CUDA_ERROR_NO_BINARY_FOR_GPU at runtime.
+    println!("cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP");
     let cuda_compute_cap = std::env::var("CUDA_COMPUTE_CAP").unwrap_or_else(|_| "80".to_string());
     let arch = format!("sm_{cuda_compute_cap}");