reasoning:
Let me analyze the situation. We have one baseline trial at depth=16 with val_bpb=0.9279. Since depth is the most important hyperparameter and has a convex optimum, I should explore different depths to locate the optimum. Let me try a shallower depth first (e.g., depth=12) to understand the landscape, keeping other params at baseline.

Actually, given the guidance that at larger FLOP budgets larger models are preferred, and we're at 3e18 FLOPs, let me try depth=20 to see if going deeper helps.

content:
## Analysis

Only one trial so far — the baseline at `depth=16`, `val_bpb=0.9279`. The priority guidance is clear: **locate the convex optimum in `depth` before tuning anything else.** With a single point we have no directional information yet, so I'll make a moderate jump upward in depth (since 3e18 FLOPs is a large budget where larger models tend to be more compute-efficient) while holding everything else at baseline. This gives us a second anchor point to determine whether the optimum is above or below depth=16.


reasoning: The baseline at depth=16 gives val_bpb=0.9280, while depth=20 gives 0.9405 — worse. So increasing depth hurt. This suggests the optimal depth is at or below 16. Let me try depth=12 to explore the other direction.

content:
## Analysis

- **depth=16** → val_bpb = 0.9280 (baseline, best so far)
- **depth=20** → val_bpb = 0.9405 (worse — larger model sees too few tokens at this FLOP budget)

The result clearly shows that at 3e18 FLOPs, going *wider/deeper* hurts. This is consistent with Chinchilla-style reasoning: at this compute budget, a smaller model trained on more tokens wins. The optimum is likely **below** depth=16. I'll now probe **depth=12** (d_model=768) to bracket the optimum from the other side, keeping all other parameters fixed at the baseline.


reasoning:
Let me analyze the results so far:

- depth=16: val_bpb = 0.9280 (best)
- depth=12: val_bpb = 0.9369
- depth=20: val_bpb = 0.9405

The results clearly show depth=16 is the best so far, with both shallower (12) and deeper (20) models performing worse. This suggests depth=16 is near the optimum, but I should verify by trying depth=14 or depth=18 to bracket it more tightly. Actually, given that depth=12 and depth=20 are both worse, and the optimum appears bracketed around 16, let me now explore other hyperparameters.
Wait, looking more carefully at the gaps—the improvement from depth=12 to depth=16 is smaller than from depth=16 to depth=20, which suggests the optimum might actually sit between 12 and 16. Let me test depth=14 to narrow down where the peak really is.

content:
## Analysis

| depth | val_bpb |
|-------|---------|
| 12 | 0.9369 |
| **16** | **0.9280** ← best |
| 20 | 0.9405 |

The depth optimum is clearly bracketed — depth=16 wins over both 12 and 20, so it's near the sweet spot. However, the drop from 12→16 (~0.009) is bigger than the rise from 16→20 (~0.013), which doesn't rule out a slightly shallower optimum between 12 and 16. I should try **depth=14** to pin down the optimum precisely before moving on to tune `matrix_lr` or the schedule.


reasoning:
Current results ranked by val_bpb:
- depth=16: 0.9279 (best)
- depth=14: 0.9292
- depth=12: 0.9369
- depth=20: 0.9405

So depth=16 is best, depth=14 is second best. The optimum appears to be around depth=16. Let me try depth=18 to bracket the optimum between 16 and 20, or maybe try depth=17 or 15. Actually, we have 14 and 16 both close, with 16 slightly better.

The pattern shows depth=16 performing best so far, with performance degrading at both depth=14 and depth=20. I'll test depth=18 to confirm whether 16 is the true optimum or if there's still room for improvement in that range, then decide whether to fine-tune other parameters at the optimal depth.

content:
## Analysis

Current results ranked by val_bpb:
| depth | val_bpb |
|-------|---------|
| 16 | **0.9280** ← best |
| 14 | 0.9292 |
| 12 | 0.9369 |
| 20 | 0.9405 |

The pattern is clear: val_bpb improves from depth=12→16, then degrades at depth=20. The optimum appears to be near depth=16, but I haven't checked depth=18 yet. Since 14 < 16 < 20 are all tested, and 16 beats both neighbors, I should verify that 16 is truly the peak (not 17 or 18) before locking it in and tuning other params. A quick depth=18 probe will fully bracket the optimum — if it's worse than 16 (as expected), we can confidently pin depth=16 and move on to `matrix_lr`.


reasoning:
Looking at the results so far:

depth=12: 0.9369
depth=14: 0.9292
depth=16: 0.9280 (baseline, best)
depth=18: 0.9354
depth=20: 0.9405

The optimum appears to be around depth=16, with depth=14 being the second best. The convex optimum is clearly around depth=15-16. Let me try depth=15 to narrow it down, while keeping all other parameters the same.

content:
## Analysis

Looking at the depth sweep so far:

| depth | val_bpb |
|-------|---------|
| 12 | 0.9369 |
| 14 | 0.9292 |
| **16** | **0.9280** ← best |
| 18 | 0.9354 |
| 20 | 0.9405 |

The val_bpb curve is clearly convex, and the minimum is sitting right around **depth=16**. Depth=14 is the closest runner-up (0.9292 vs 0.9280), while depth=18 is already noticeably worse. This suggests the true optimum might be between 14 and 16 — specifically depth=15 is untested and could beat the baseline. Let me probe that gap before moving on to tune other hyperparameters.