diff --git a/nodes/selva_lora_trainer.py b/nodes/selva_lora_trainer.py
index d1b9911..f88820b 100644
--- a/nodes/selva_lora_trainer.py
+++ b/nodes/selva_lora_trainer.py
@@ -88,21 +88,24 @@ def _load_npz(path: Path) -> dict:
 # ---------------------------------------------------------------------------
 
 def _eval_sample(generator, feature_utils_orig, dataset, seq_cfg, device, dtype,
-                 num_steps: int = 8):
-    """Run a quick no-CFG inference pass on a random training clip.
+                 num_steps: int = 8, seed: int = 0):
+    """Run a quick no-CFG inference pass on a fixed training clip.
 
+    Always uses dataset[0] and a fixed noise seed so samples across checkpoints
+    are directly comparable — you can hear the model improve step by step.
     Returns (waveform [1, L] float32 cpu, sample_rate) or (None, None) on failure.
     Uses fewer ODE steps than inference (8 vs 25) for speed.
     """
     generator.eval()
     try:
-        _, clip_f_cpu, sync_f_cpu, text_clip_cpu = random.choice(dataset)
+        _, clip_f_cpu, sync_f_cpu, text_clip_cpu = dataset[0]
         clip_f    = clip_f_cpu.to(device, dtype)
         sync_f    = sync_f_cpu.to(device, dtype)
         text_clip = text_clip_cpu.to(device, dtype)
 
+        rng = torch.Generator(device=device).manual_seed(seed)
         x0 = torch.randn(1, seq_cfg.latent_seq_len, generator.latent_dim,
-                         device=device, dtype=dtype)
+                         device=device, dtype=dtype, generator=rng)
 
         eval_fm = FlowMatching(min_sigma=0, inference_mode="euler", num_steps=num_steps)
 
@@ -664,11 +667,13 @@ class SelvaLoraTrainer:
                     }, ckpt_path)
                     print(f"[LoRA Trainer] Saved {ckpt_path}", flush=True)
 
-                    # Save a quick eval sample next to the checkpoint
+                    # Save a quick eval sample in samples/ subfolder
+                    samples_dir = output_dir / "samples"
+                    samples_dir.mkdir(exist_ok=True)
                     wav, sr = _eval_sample(generator, feature_utils_orig,
-                                           dataset, seq_cfg, device, dtype)
+                                           dataset, seq_cfg, device, dtype, seed=seed)
                     if wav is not None:
-                        wav_path = output_dir / f"sample_step{step:05d}.wav"
+                        wav_path = samples_dir / f"step_{step:05d}.wav"
                         try:
                             torchaudio.save(str(wav_path), wav, sr)
                         except RuntimeError: