reasoning:
Let me analyze the current situation. We have one baseline result at val_bpb = 0.9427 with depth=8, matrix_lr=0.03, warmup_ratio=0.0, warmdown_ratio=0.4.

My strategy:
1. First, bracket depth with small moves (depth=6 and depth=10, or depth=7 and depth=10)
2. Keep other params at baseline for now

Let me propose two configs to bracket depth. I'll go with depth=6 and depth=11 to bracket the optimum. Actually, I should propose one at a time... wait, I can make multiple independent calls.
I'll run two trials in parallel—one dropping depth to 6 and another pushing it to 11—to quickly identify which direction improves performance while holding everything else constant.

content: Looking at the baseline, I have one result at depth=8. The first priority is to bracket the depth optimum with small moves — I'll probe depth=6 and depth=11 simultaneously (keeping all other parameters at baseline values) to quickly determine which direction the convex optimum lies.


reasoning:
The results so far show that depth=6 is better than depth=8 at this FLOP scale. I should bracket depth by trying depth=4 and depth=8 (already done). Let me try depth=4 to see if the optimum is even shallower, or maybe depth=5 to bracket more precisely. Since depth=6 is better than depth=8, I should try depth=4 or depth=5 to bracket the optimum.

Let me try depth=4 to bracket downward.

content:
Looking at the results so far:
- **depth=8**: val_bpb = 0.9427 (baseline)
- **depth=6**: val_bpb = 0.8645 (best, significant improvement)

depth=6 is clearly better than depth=8, suggesting the FLOP-optimal model is shallower at 1e17. I need to bracket the optimum by probing **downward** — try depth=4 to see if the trend continues or reverses. This is a small local move of 2 layers, keeping everything else fixed so the result is fully attributable to depth.


reasoning:
Let me analyze the results so far:

- depth=8: val_bpb=0.9427
- depth=6: val_bpb=0.8645 (best)
- depth=4: val_bpb=0.8796

So depth=6 is currently the best. We have depth=4 being worse than depth=6, and depth=8 being worse than depth=6. This suggests depth=6 might be near the optimum, but I should check depth=5 and depth=7 to bracket it more precisely. Actually, depth=4 < depth=6 < depth=8, and depth=6 is best.

I'm seeing a clear pattern where depth=6 outperforms both shallower and deeper trees, so I should test depth=5 and depth=7 to narrow down the optimal range.

content:
## Analysis

The results so far show a clear picture:
- depth=4: 0.8796
- **depth=6: 0.8645** ← best
- depth=8: 0.9427

The convex optimum in depth is bracketed: depth=6 beats both depth=4 and depth=8. The gap between depth=6 and depth=4 is smaller than the gap to depth=8, so depth=6 is likely optimal or close to it. I could check depth=5 or depth=7, but the difference between 4 and 6 is modest — let me **pin depth=6** and begin exploring the learning rate, which is the next biggest lever.

The default `matrix_lr=0.03` came from the prior scale. At 1e17 FLOPs with a shallower model, the optimal Muon LR may be different. I'll probe **matrix_lr=0.01** (a ~3× decrease), which is a moderate first step on the log scale.


reasoning:
Let me analyze the results so far:

1. depth=8, matrix_lr=0.03: 0.9427
2. depth=6, matrix_lr=0.03: 0.8645
3. depth=4, matrix_lr=0.03: 0.8796
4. depth=6, matrix_lr=0.01: 0.8559

So depth=6 is better than both depth=8 and depth=4, confirming it's near the optimum. The best so far is depth=6 with matrix_lr=0.01 (0.8559).
The pattern suggests lower matrix_lr values help at depth=6, so I should test matrix_lr=0.003 to see if that trend continues. Depth appears well-bracketed around 6 since both shallower and deeper values perform worse.

content:
## Analysis

| depth | matrix_lr | val_bpb |
|-------|-----------|---------|
| 8 | 0.03 | 0.9427 |
| 6 | 0.03 | 0.8645 |
| 4 | 0.03 | 0.8796 |
| 6 | 0.01 | **0.8559** ← best |

**Conclusions so far:**
- **Depth is bracketed**: depth=6 beats both depth=4 and depth=8 — it sits at the convex optimum. Pinning it now.
- **`matrix_lr` trend is clear and not yet reversed**: cutting from 0.03 → 0.01 gave a solid improvement (+0.0086 bpb). The log-scale sensitivity suggests trying another ~3× reduction to **0.003** is the most informative next step — it will either confirm the downward trend or show we've overshot the optimum.
- Schedule knobs (`warmup_ratio`=0.0, `warmdown_ratio`=0.4) are untouched; keep them fixed while we bracket `matrix_lr` first.


reasoning:
Let me analyze the results so far:

1. depth=8, matrix_lr=0.03: 0.9427
2. depth=6, matrix_lr=0.03: 0.8645
3. depth=4, matrix_lr=0.03: 0.8796
4. depth=6, matrix_lr=0.01: 0.8559 ← best
5. depth=6, matrix_lr=0.003: 0.9169

So depth=6 is clearly better than 4 or 8.

The learning rate pattern shows 0.01 performing best, with 0.003 too conservative and 0.03 slightly too aggressive. I should explore the middle ground between 0.01 and 0.03, testing something like 0.015 or 0.02 to find the sweet spot.

Depth appears to have a clear optimum at 6, so I'll lock that in and focus on tuning other hyperparameters like warmdown_ratio—the default 0.4 might benefit from adjustment upward to 0.5 or 0.6, or possibly lower. Let me run matrix_lr=0.02 with depth=6 to narrow down the learning rate range more precisely.

content:
## Analysis

Current results at `depth=6`:
| matrix_lr | val_bpb |
|-----------|---------|
| 0.003 | 0.9169 |
| 0.03 | 0.8645 |
| 0.01 | **0.8559** ← best |

The LR optimum is clearly around 0.01, with 0.003 too low and 0.03 slightly too high. The curve between 0.01 and 0.03 needs one more point — 0.02 — to properly bracket the optimum. Depth is pinned at 6 (better than both 4 and 8). I'll keep warmup/warmdown at their current values while I finish nailing down matrix_lr.


reasoning:
Let me analyze the results so far:

1. depth=8, matrix_lr=0.03: 0.9427 (baseline)
2. depth=6, matrix_lr=0.03: 0.8645
3. depth=4, matrix_lr=0.03: 0.8796
4. depth=6, matrix_lr=0.01: 0.8559
5. depth=6, matrix_lr=0.003: 0.9169
6. depth=6, matrix_lr=0.02: 0.8546 ← best
7. depth=6, matrix_lr=0.015: (testing)

Depth=6 is clearly outperforming the other depths, and the learning rate around 0.02 looks optimal based on the pattern I'm seeing. I should test matrix_lr=0.015 to narrow down the sweet spot, or try depth=5 to bracket the depth parameter more precisely.

Now I want to explore the schedule parameters. I've been using warmup_ratio=0.0 and warmdown_ratio=0.4, so let me experiment with increasing warmdown_ratio to 0.6 while keeping depth=6 and matrix_lr=0.02 to see if a longer warmdown phase improves performance.

content:
## Analysis

Current best: `depth=6, matrix_lr=0.02, warmup_ratio=0.0, warmdown_ratio=0.4` → **0.8546**

**LR landscape at depth=6:**
- 0.003 → 0.9169 (too low)
- 0.01 → 0.8559
- 0.02 → **0.8546** ← best
- 0.03 → 0.8645 (slightly too high)

The optimum looks to be right around 0.02, with the curve slightly favoring lower values over higher. The gap between 0.02 and 0.01 is small, so the optimum might sit a bit below 0.02. Let me probe **matrix_lr=0.015** next — a modest step down between the two best values — to tighten the LR bracket before moving on to schedule knobs.

Depth is bracketed (6 beats both 4 and 8), so I'll pin it at 6.


reasoning:
Looking at the results so far:

- depth=6, matrix_lr=0.015 → 0.8520 (best)
- depth=6, matrix_lr=0.02 → 0.8546
- depth=6, matrix_lr=0.01 → 0.8559
- depth=6, matrix_lr=0.03 → 0.8645
- depth=4, matrix_lr=0.03 → 0.8796
- depth=6, matrix_lr=0.003 → 0.9169
- depth=8, matrix_lr=0.03 → 0.9427

The learning rate seems to have a sweet spot around 0.015, with lower values like 0.003 and higher values like 0.03 both pushing performance in different directions. Now I want to explore the warmdown_ratio parameter—currently at 0.4—to see if adjusting the schedule could improve things further.

content:
## Analysis

The current best is `depth=6, matrix_lr=0.015, warmdown_ratio=0.4` (val_bpb=0.8520). The `matrix_lr` landscape is now well-bracketed around 0.015:

| matrix_lr | val_bpb |
|-----------|---------|
| 0.003 | 0.9169 |
| 0.01 | 0.8559 |
| 0.015 | **0.8520** |
| 0.02 | 0.8546 |
| 0.03 | 0.8645 |

The optimum is clearly near 0.015 — further LR refinement has diminishing returns. It's time to **explore the schedule knobs**, specifically `warmdown_ratio`. The default of 0.4 is a strong prior but hasn't been tested; a larger warmdown fraction often yields better final convergence. I'll try `warmdown_ratio=0.6` at the best depth and LR.


reasoning:
Let me analyze the results so far:

Best result: depth=6, matrix_lr=0.015, warmup_ratio=0.0, warmdown_ratio=0.4 → val_bpb=0.8520

The warmdown_ratio=0.6 was worse than 0.4. Let me try warmdown_ratio=0.3 or 0.5 to bracket, or try depth=5 or 7 to bracket depth.

Actually, depth hasn't been fully bracketed - we have depth=4 (0.8796), depth=6 (0.8520 best), depth=8 (0.9427).

Depth=8 was the baseline with matrix_lr=0.03, so that's not a fair comparison. I should test depth=7 with the current best learning rate to properly bracket. Looking at the results, depth=6 is clearly outperforming both depth=4 and depth=8, so that seems to be the sweet spot. Now I'll focus on tuning the schedule parameters—warmdown_ratio=0.4 beat 0.6, so let me try 0.3 to see if an even shorter warmdown helps.

content:
## Analysis

Current leaderboard:
1. **depth=6, matrix_lr=0.015, warmdown_ratio=0.4** → 0.8520 ✅ best
2. depth=6, matrix_lr=0.02, warmdown_ratio=0.4 → 0.8546
3. depth=6, matrix_lr=0.015, warmdown_ratio=**0.6** → 0.8556 ← worse than 0.4
4. depth=6, matrix_lr=0.01, warmdown_ratio=0.4 → 0.8559

**Key observations:**
- `depth=6` is clearly bracketed (4 and 8 are both worse) — pinning it.
- `matrix_lr=0.015` is the current sweet spot (between 0.01 and 0.02).
- `warmdown_ratio=0.6` hurt vs 0.4, suggesting the optimum is ≤0.4. The natural next probe is **warmdown_ratio=0.3** to bracket from below.
- `warmup_ratio=0.0` hasn't been challenged yet, but warmdown is the bigger lever; bracket it first.

**Next move:** Keep depth=6 and matrix_lr=0.015 fixed, try warmdown_ratio=0.3 to determine if the optimum lies below 0.4.


reasoning:
Let me analyze the current results. The best so far is depth=6, matrix_lr=0.015, warmdown_ratio=0.3 with val_bpb=0.8522. Let me try warmdown_ratio=0.2 to continue exploring that direction, or try depth=5 or 7 to bracket depth better. Actually depth was bracketed with 4 and 6, showing 6 is better than 4. I haven't tried depth=7 or 8 at matrix_lr=0.015. But the baseline at depth=8 was 0.9427, so depth=6 is clearly better. Maybe try depth=5 or 7.
Looking at the warmdown progression, I'm seeing diminishing returns as I decrease from 0.6 to 0.3, with the last step being nearly flat. I should test whether pushing to 0.2 continues that trend, or shift focus to tuning matrix_lr upward to 0.018 with the current best configuration.

content:
## Analysis

Current best: `depth=6, matrix_lr=0.015, warmup_ratio=0.0, warmdown_ratio=0.3` → **0.8522**

Key observations:
- **Depth bracketed**: depth=4 (0.8796) > depth=8 baseline (0.9427) >> depth=6 (best). No need to re-litigate depth.
- **matrix_lr**: Clear optimum near 0.015, with 0.01→0.015→0.02 showing a valley. Best at 0.015.
- **warmdown_ratio**: 0.6 (0.8556) → 0.4 (0.8520) → 0.3 (0.8522) — essentially flat between 0.3 and 0.4, suggesting the optimum is around 0.3–0.4. Going lower to 0.2 is worth one probe.

**Next step**: Try `warmdown_ratio=0.2` to probe whether the slight improvement from 0.4→0.3 continues, or if 0.3 is already at the floor. Keep everything else at the current best.


reasoning:
Let me analyze the results so far:

Best result: depth=6, matrix_lr=0.015, warmup_ratio=0.0, warmdown_ratio=0.4 → 0.8519772
Second best: depth=6, matrix_lr=0.015, warmup_ratio=0.0, warmdown_ratio=0.3 → 0.8521507

The warmdown_ratio sweep shows:
- 0.6: 0.8556
- 0.4: 0.8520
- 0.3: 0.8522
- 0.2: 0.8537
The optimal warmdown_ratio appears to be in the 0.3-0.4 range, though 0.6 and 0.2 are also competitive. Since matrix_lr at 0.015 is performing well compared to nearby values like 0.01 and 0.02, I should explore variations around this point more carefully. I should focus on the learning rate parameters since I have limited trials left. Let me test matrix_lr=0.012 paired with warmdown_ratio=0.4 to narrow down the optimal learning rate.

content:
## Analysis

Current best: `depth=6, matrix_lr=0.015, warmup_ratio=0.0, warmdown_ratio=0.4` → **0.8520**

The warmdown sweep shows:
- 0.6 → 0.8556 (worse)
- 0.4 → **0.8520** (best)
- 0.3 → 0.8522 (very close)
- 0.2 → 0.8537 (worse)

The optimum sits between 0.3–0.4, with 0.4 marginally winning. The matrix_lr sweep found 0.015 as the peak, with 0.01 and 0.02 both slightly worse. Let me probe one step finer on `matrix_lr` — trying 0.012 to see if the true optimum is slightly below 0.015, since 0.01 gave 0.8559 and 0.015 gave 0.8520, suggesting the curve may still be descending through 0.012.


reasoning:
Let me analyze the results so far.

Best result: depth=6, matrix_lr=0.012, warmdown_ratio=0.4, val_bpb=0.8514

The matrix_lr search shows: 0.003 (bad), 0.01 (0.8559), 0.012 (0.8514), 0.015 (0.8520), 0.02 (0.8546), 0.03 (0.8645)

So the optimum is around 0.012. Let me try 0.010-0.012 range more carefully, or try 0.010 with warmdown_ratio=0.3 since that also did slightly better.
Comparing the warmdown_ratio values at matrix_lr=0.012, I see 0.4 gives the best performance at 0.8514, while 0.3 and 0.6 are both slightly worse, so I should focus on refining around this sweet spot.

content:
Looking at the trial history:

- **Depth**: Bracketed at 6 (better than 4 and 8) — pin it.
- **matrix_lr**: Clear optimum emerging around 0.012 (0.8514), better than 0.010 (0.8559) and 0.015 (0.8520). The curve is shallow but consistently points to ~0.011–0.013.
- **warmdown_ratio**: With matrix_lr=0.015, warmdown=0.4 did best (vs 0.3, 0.2, 0.6). Now that matrix_lr is pinned at 0.012, worth checking if a slightly shorter warmdown (0.35) gives a small gain — the 0.3 result with lr=0.015 was close to 0.4.

Next step: fine-tune warmdown_ratio slightly around the best (0.4 → 0.35) while keeping the best matrix_lr=0.012.