diff --git a/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs b/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
index cbaa3531e..a9b7f79d6 100644
--- a/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
+++ b/crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs
@@ -642,6 +642,16 @@ pub struct GpuDqnTrainer {
     zero_kernel: CudaFunction,
     regime_scale_kernel: CudaFunction,
     shrink_perturb_kernel: CudaFunction,
+    /// Ungraphed variant of shrink_perturb — from a separate CUmodule.
+    /// shrink_and_perturb() runs at epoch boundaries (after graph capture).
+    /// On Hopper, launching ANY CUfunction from a CUmodule that also has
+    /// graph-captured CUfunctions corrupts graph kernel state (3100ms replay).
+    shrink_perturb_ungraphed: CudaFunction,
+    /// Ungraphed variant of scale_f32 — from a separate CUmodule.
+    /// scale_adam_momentum() runs at cosine LR warm restarts (after graph capture).
+    /// scale_f32_kernel is captured in forward_child; launching from the same
+    /// CUmodule ungraphed corrupts the child graph on Hopper.
+    scale_f32_ungraphed: CudaFunction,
     relu_mask_kernel: CudaFunction,
     spectral_norm_batched_kernel: CudaFunction,
     /// Descriptor buffer for batched spectral norm: 12 matrices x 6 u64 = 72 elements.
@@ -1492,8 +1502,11 @@ impl GpuDqnTrainer {
         let blocks = ((self.total_params + 255) / 256) as u32;
         let cfg = LaunchConfig { grid_dim: (blocks, 1, 1), block_dim: (256, 1, 1), shared_mem_bytes: 0 };
         let m_ptr = self.m_buf.raw_ptr();
+        // Use scale_f32_ungraphed — called at cosine LR warm restarts (after graph capture).
+        // scale_f32_kernel is captured in forward_child; launching from the same CUmodule
+        // ungraphed corrupts the child graph's kernel state on Hopper (3100ms replay).
         unsafe {
-            self.stream.launch_builder(&self.scale_f32_kernel)
+            self.stream.launch_builder(&self.scale_f32_ungraphed)
                 .arg(&m_ptr)
                 .arg(&scale)
                 .arg(&n)
@@ -4055,11 +4068,15 @@ impl GpuDqnTrainer {
         // GPU-native: single kernel generates noise + blends in one pass on params_buf.
         // params_buf[i] = alpha * params_buf[i] + (1-alpha) * N(0, sigma)
         // Branch heads [skip_start, skip_end) are excluded from perturbation.
+        // Use shrink_perturb_ungraphed — this runs AFTER graph capture (epoch boundary),
+        // and shrink_perturb_kernel is in the same CUmodule as graphed kernels.
+        // On Hopper, launching any CUfunction from a graphed CUmodule ungraphed
+        // corrupts the child graph's kernel state (3100ms replay).
         let _evt_guard = EventTrackingGuard::new(self.stream.context());
         let params_ptr = self.params_buf.raw_ptr();
         unsafe {
             self.stream
-                .launch_builder(&self.shrink_perturb_kernel)
+                .launch_builder(&self.shrink_perturb_ungraphed)
                 .arg(&params_ptr)
                 .arg(&alpha)
                 .arg(&sigma)
@@ -4106,7 +4123,7 @@ impl GpuDqnTrainer {
         // per array. Stack is set once in DQNTrainer::new() (64KB for all kernels).
 
         // ── Compile utility kernels (grad_norm, adam_update, etc.) ─
-        let (grad_norm_kernel, grad_norm_finalize_kernel, grad_norm_finalize_adam, grad_norm_finalize_post_aux, adam_update_kernel, adam_update_post_aux, scale_f32_post_aux, saxpy_kernel, zero_kernel, regime_scale_kernel, shrink_perturb, _relu_mask_in_module, spectral_norm_kernel, clip_grad_kernel, pad_states_kernel, saxpy_f32_kernel, scale_f32_kernel, stochastic_depth_kernel, stochastic_depth_rng_kernel, bn_tanh_backward_kernel, bn_bias_grad_kernel, bn_tanh_concat_kernel_fn, vaccine_dot_kernel, vaccine_project_kernel, vaccine_dot_finalize, causal_intervene_kernel_fn, causal_reduce_kernel_fn, causal_mean_reduce_kernel_fn, pruning_mask_kernel, pruning_compute_kernel, her_inplace_kernel, nan_check_f32_kernel, nan_check_f32_kernel_b, popart_normalize_kernel, saxpy_f32_adam_grad, saxpy_f32_aux, grad_norm_standalone_post_aux) =
+        let (grad_norm_kernel, grad_norm_finalize_kernel, grad_norm_finalize_adam, grad_norm_finalize_post_aux, adam_update_kernel, adam_update_post_aux, scale_f32_post_aux, saxpy_kernel, zero_kernel, regime_scale_kernel, shrink_perturb, _relu_mask_in_module, spectral_norm_kernel, clip_grad_kernel, pad_states_kernel, saxpy_f32_kernel, scale_f32_kernel, stochastic_depth_kernel, stochastic_depth_rng_kernel, bn_tanh_backward_kernel, bn_bias_grad_kernel, bn_tanh_concat_kernel_fn, vaccine_dot_kernel, vaccine_project_kernel, vaccine_dot_finalize, causal_intervene_kernel_fn, causal_reduce_kernel_fn, causal_mean_reduce_kernel_fn, pruning_mask_kernel, pruning_compute_kernel, her_inplace_kernel, nan_check_f32_kernel, nan_check_f32_kernel_b, popart_normalize_kernel, saxpy_f32_adam_grad, saxpy_f32_aux, grad_norm_standalone_post_aux, shrink_perturb_ungraphed, scale_f32_ungraphed) =
             compile_training_kernels(&stream, &config)?;
 
         // Separate grad_norm instance for non-graph launches (outside CUDA graph).
@@ -5720,6 +5737,8 @@ impl GpuDqnTrainer {
             zero_kernel,
             regime_scale_kernel,
             shrink_perturb_kernel: shrink_perturb,
+            shrink_perturb_ungraphed,
+            scale_f32_ungraphed,
             relu_mask_kernel: relu_mask_standalone,
             spectral_norm_batched_kernel: spectral_norm_kernel,
             spectral_norm_descriptors,
@@ -10302,7 +10321,7 @@ impl GpuDqnTrainer {
 fn compile_training_kernels(
     stream: &Arc<CudaStream>,
     config: &GpuDqnTrainConfig,
-) -> Result<(CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction), MLError> {
+) -> Result<(CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction), MLError> {
     info!(
         state_dim = config.state_dim,
         total_params = compute_total_params(config),
@@ -10372,15 +10391,26 @@ fn compile_training_kernels(
         .map_err(|e| MLError::ModelError(format!("spectral_norm_batched load: {e}")))?;
     let clip_grad = module.load_function("dqn_clip_grad_kernel")
         .map_err(|e| MLError::ModelError(format!("dqn_clip_grad_kernel load: {e}")))?;
-    // pad_states + stochastic_depth_rng run UNGRAPHED every step. They MUST be from
-    // a separate CUmodule — on Hopper, launching ANY CUfunction from a CUmodule that
-    // also has graph-captured CUfunctions corrupts the graph's kernel state (3100ms replay).
+    // Ungraphed kernels MUST be from a separate CUmodule — on Hopper, launching ANY
+    // CUfunction from a CUmodule that also has graph-captured CUfunctions corrupts
+    // the graph's kernel state (3100ms replay). All kernels launched outside graph
+    // capture (per-step or at epoch boundaries) must use handles from this module.
     let ungraphed_module = context.load_cubin(DQN_UTILITY_CUBIN.to_vec())
         .map_err(|e| MLError::ModelError(format!("ungraphed utility cubin load: {e}")))?;
     let pad_states = ungraphed_module.load_function("pad_states_kernel")
         .map_err(|e| MLError::ModelError(format!("pad_states_kernel load: {e}")))?;
     let stochastic_depth_rng = ungraphed_module.load_function("stochastic_depth_rng")
         .map_err(|e| MLError::ModelError(format!("stochastic_depth_rng load: {e}")))?;
+    // shrink_and_perturb() is called at epoch boundaries (after graph capture).
+    // dqn_shrink_perturb_kernel lives in the same CUmodule as other graphed kernels
+    // (scale_f32, saxpy_f32, spectral_norm, adam_update, etc.) — so it MUST be
+    // loaded from ungraphed_module to prevent Hopper CUmodule corruption.
+    let shrink_perturb_ungraphed = ungraphed_module.load_function("dqn_shrink_perturb_kernel")
+        .map_err(|e| MLError::ModelError(format!("shrink_perturb_ungraphed load: {e}")))?;
+    // scale_adam_momentum() is called at cosine LR warm restarts (after graph capture).
+    // dqn_scale_f32_kernel is captured in forward_child — same CUmodule as above.
+    let scale_f32_ungraphed = ungraphed_module.load_function("dqn_scale_f32_kernel")
+        .map_err(|e| MLError::ModelError(format!("scale_f32_ungraphed load: {e}")))?;
     // stochastic_depth_scale is GRAPHED (inside forward_child via apply_regime_dropout)
     // — stays in the main module.
     let stochastic_depth = module.load_function("stochastic_depth_scale")
@@ -10418,8 +10448,8 @@ fn compile_training_kernels(
     let popart_normalize = module.load_function("popart_normalize_kernel")
         .map_err(|e| MLError::ModelError(format!("popart_normalize_kernel load: {e}")))?;
 
-    info!("GpuDqnTrainer: 33 utility kernels loaded from precompiled cubin (5 CUmodules)");
-    Ok((grad_norm, grad_norm_finalize, grad_norm_finalize_adam, grad_norm_finalize_post_aux, adam_update, adam_update_post_aux, scale_f32_post_aux, saxpy, zero, regime_scale, shrink_perturb, _relu_mask_from_module, spectral_norm, clip_grad, pad_states, saxpy_f32, scale_f32, stochastic_depth, stochastic_depth_rng, bn_tanh_bw, bn_bias_grad, bn_tanh_concat, vaccine_dot, vaccine_project, vaccine_dot_finalize, causal_intervene, causal_reduce, causal_mean_reduce, pruning_mask_fn, pruning_compute_fn, her_inplace, nan_check_f32, nan_check_f32_b, popart_normalize, saxpy_f32_adam_grad, saxpy_f32_aux, grad_norm_standalone_post_aux))
+    info!("GpuDqnTrainer: 35 utility kernels loaded from precompiled cubin (5 CUmodules)");
+    Ok((grad_norm, grad_norm_finalize, grad_norm_finalize_adam, grad_norm_finalize_post_aux, adam_update, adam_update_post_aux, scale_f32_post_aux, saxpy, zero, regime_scale, shrink_perturb, _relu_mask_from_module, spectral_norm, clip_grad, pad_states, saxpy_f32, scale_f32, stochastic_depth, stochastic_depth_rng, bn_tanh_bw, bn_bias_grad, bn_tanh_concat, vaccine_dot, vaccine_project, vaccine_dot_finalize, causal_intervene, causal_reduce, causal_mean_reduce, pruning_mask_fn, pruning_compute_fn, her_inplace, nan_check_f32, nan_check_f32_b, popart_normalize, saxpy_f32_adam_grad, saxpy_f32_aux, grad_norm_standalone_post_aux, shrink_perturb_ungraphed, scale_f32_ungraphed))
 }
 
 /// Load the standalone Polyak EMA kernel from precompiled cubin.