sync experiments

dust_mechanistic/LOG.md

@@ -205,3 +205,17 @@ Same ordering at init. Same # of forward passes as base. -> promote allR to full
   1M ckpt: plateaus at .8. Small sigma collapses => the CV residual is dominated by a numerical (bf16 forward)
   noise floor, not nonlinearity; DUST's sigma=.2 was tuned without a CV. Without CV sigma=.4 also helps, less.
 - Launched 10M CV sigma=.8 full (`runs/f10M_cv_s8`) and quarter draws (`runs/f10M_cvq_s8`). CV-quarter sigma=.2 10M running.
+
+## 2026-10-11 ~02:00 — 10M progress; quarter draws ≈ full draws
+| step | DUST | CV full (σ.2) | CV quarter (σ.2) | CV full σ.8 | CV quarter σ.8 | backprop |
+|---|---|---|---|---|---|---|
+| 50 | 6.264 | 6.175 | 6.177 | 6.203 | 6.194 | 6.074 |
+| 100 | 5.845 | 5.698 | 5.696 | – | 5.689 | 5.550 |
+| 200 | 5.518 | 5.363 | 5.366 | – | – | 5.263 |
+- CV with 1/4 writer draws tracks full-draw CV almost exactly at 10M => 2.5x cheaper at equal loss.
+- sigma .8 better cosines but no loss gain so far. => remaining estimator VARIANCE no longer limits training;
+  the residual gap to backprop must come from BIAS in some group(s) or from the optimizer.
+- Launched hybrid-oracle ablation (`runs/hy_*`, 10M data, 100 steps, CV-quarter, replace one group's estimate by
+  the backprop gradient): none / wte / writers / c_fc / attention internals / lambdas / all.
+- 10M CV-quarter σ.2 (`runs/f10M_cvq`) FINAL: Best validation: 5.1394; test: 5.1256 (CV full: 5.0980/5.0836; DUST 5.2441/5.2338) at 2.07 s/step vs DUST 5.16.
+  CORRECTION: quarter ≈ full only through ~step 300; late in training full draws win (5.098 vs 5.139) — variance matters again late.

dust_mechanistic/dustx.py

@@ -451,7 +451,18 @@ def main():
     for step in range(1, steps + 1):
         batch = training[(step - 1) * 8:step * 8].to(device)
         bx, by = batch[:, :-1].contiguous(), batch[:, 1:].contiguous()
-        if opts.get('oracle_only'):
+        if opts.get('oracle_groups'):  # diagnostic: replace listed groups' estimates by backprop gradients
+            from gradlab import backprop_grad, group_of
+            gbp = backprop_grad(model, bx, by)
+            grads = est.gradients(bx, by)
+            optimizer.zero_grad(set_to_none=True)
+            for name, parameter in model.named_parameters():
+                src_g = gbp if group_of(name) in opts['oracle_groups'] else grads
+                parameter.grad = src_g[name].to(parameter.dtype)
+            optimizer.step()
+            loss = est.clean_loss[by != -1].mean().item()
+            est.last_stats = {}
+        elif opts.get('oracle_only'):
             from gradlab import backprop_grad
             gbp = backprop_grad(model, bx, by)
             optimizer.zero_grad(set_to_none=True)

dust_mechanistic/logs/hy_fc.log

@@ -0,0 +1,47 @@
+10M: 100 steps opts={'analytic_v': 1, 'analytic_gate': 1, 'fc_from_writer': 'replace', 'hub_from_writer': 'replace', 'v_gamma': 1.0, 'analytic_qk': 1, 'qk_gamma': 1.0, 'analytic_head': 1, 'writer_cv': 1, 'wte_cv': 1, 'recipe': {'writers': [22, 18, 10, 6, 4, 2, 2, 2], 'hidden': [0, 0, 0, 0, 0, 0, 0, 0], 'hubs': [0, 0, 0, 0, 0, 0, 0, 0]}, 'oracle_groups': ['mlp.c_fc']} val=8.3182
+{"step": 1, "train_loss": 8.3181734085083, "elapsed_seconds": 8.798312628998247, "val_loss": 8.196017040925867}
+{"step": 2, "train_loss": 8.195629119873047, "elapsed_seconds": 22.576933205000387}
+{"step": 3, "train_loss": 7.9539947509765625, "elapsed_seconds": 31.197956702999363}
+{"step": 4, "train_loss": 7.56043815612793, "elapsed_seconds": 40.21773596300045}
+{"step": 5, "train_loss": 8.003191947937012, "elapsed_seconds": 49.03116422899984}
+{"step": 6, "train_loss": 7.447507381439209, "elapsed_seconds": 57.57956204199945}
+{"step": 7, "train_loss": 7.584412574768066, "elapsed_seconds": 66.39135012800034}
+{"step": 8, "train_loss": 7.605735778808594, "elapsed_seconds": 74.9711866639991}
+{"step": 9, "train_loss": 7.511780738830566, "elapsed_seconds": 83.82699288000003}
+{"step": 10, "train_loss": 7.377230644226074, "elapsed_seconds": 92.86990464599876}
+{"step": 11, "train_loss": 7.241350173950195, "elapsed_seconds": 101.55805995799892}
+{"step": 12, "train_loss": 7.29730224609375, "elapsed_seconds": 110.37272489300085}
+{"step": 13, "train_loss": 7.262905597686768, "elapsed_seconds": 119.13060109100115}
+{"step": 14, "train_loss": 7.147160530090332, "elapsed_seconds": 127.71721831600007}
+{"step": 15, "train_loss": 7.090184688568115, "elapsed_seconds": 136.5385135789984}
+{"step": 16, "train_loss": 7.058330059051514, "elapsed_seconds": 145.2167425539992}
+{"step": 17, "train_loss": 7.100293159484863, "elapsed_seconds": 154.1585095219998}
+{"step": 18, "train_loss": 6.956493377685547, "elapsed_seconds": 162.95236352099892}
+{"step": 19, "train_loss": 6.913512229919434, "elapsed_seconds": 171.5087333429983}
+{"step": 20, "train_loss": 6.843237400054932, "elapsed_seconds": 180.53893495100056}
+{"step": 21, "train_loss": 6.916533946990967, "elapsed_seconds": 189.09202129299956}
+{"step": 22, "train_loss": 6.784431457519531, "elapsed_seconds": 197.93335414199828}
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+{"step": 39, "train_loss": 6.431987285614014, "elapsed_seconds": 346.7992841339983}
+{"step": 40, "train_loss": 6.320334434509277, "elapsed_seconds": 355.7800956430001}
+{"step": 41, "train_loss": 6.439239501953125, "elapsed_seconds": 364.70043216499835}
+{"step": 42, "train_loss": 6.325399398803711, "elapsed_seconds": 373.2624951049984}
+{"step": 43, "train_loss": 6.264777183532715, "elapsed_seconds": 382.0227796219988}
+{"step": 44, "train_loss": 6.230187892913818, "elapsed_seconds": 388.51097611300065}
+{"step": 45, "train_loss": 6.267449378967285, "elapsed_seconds": 395.1870814620015}
+{"step": 46, "train_loss": 6.295267105102539, "elapsed_seconds": 402.1153354570015}

dust_mechanistic/logs/hy_none.log

@@ -0,0 +1,102 @@
+10M: 100 steps opts={'analytic_v': 1, 'analytic_gate': 1, 'fc_from_writer': 'replace', 'hub_from_writer': 'replace', 'v_gamma': 1.0, 'analytic_qk': 1, 'qk_gamma': 1.0, 'analytic_head': 1, 'writer_cv': 1, 'wte_cv': 1, 'recipe': {'writers': [22, 18, 10, 6, 4, 2, 2, 2], 'hidden': [0, 0, 0, 0, 0, 0, 0, 0], 'hubs': [0, 0, 0, 0, 0, 0, 0, 0]}} val=8.3182
+{"step": 1, "train_loss": 8.3181734085083, "elapsed_seconds": 9.573115973998938, "gnorm": {"resid_lambdas": 0.00089, "x0_lambdas": 0.00096, "transformer": 0.00106, "c_q": 0.00324, "c_k": 0.00323, "c_v": 0.01874, "c_proj": 0.06392, "c_fc": 0.05229, "ve_gate": 2e-05, "lm_head": 0.89047, "value_embeds": 0.00013}, "beta": [[1.017], [1.04], [1.0], [1.002], [0.997], [1.004], [0.997], [0.994], [1.01], [1.004], [1.006], [0.99], [1.001], [0.992], [0.998], [1.002], [1.795]], "val_loss": 8.19602943869198}
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dust_mechanistic/logs/hy_writers.log

@@ -0,0 +1,102 @@
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dust_mechanistic/logs/hy_wte.log

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+Best validation: 5.6226; test: 5.6113

dust_mechanistic/runs/f10M_cv_s8/config.json

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dust_mechanistic/runs/f10M_cvq_s8/config.json

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dust_mechanistic/runs/hy_fc/config.json

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dust_mechanistic/runs/hy_fc/metrics.jsonl

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dust_mechanistic/runs/hy_none/config.json

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dust_mechanistic/runs/hy_writers/config.json

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