Finished
RosentWinternet_502_24x64 *diff8.0+0.08LLR: -2.96 (-2.94, 2.94) [0.00, 5.00]
Games: 6688 W: 1605 L: 1709 D: 3374
Ptnml(0-2): 168, 856, 1382, 788, 150
Warmup Stable Decay seems to fit the training pipeline better than the previous linear scheduler based on metrics. Does this hold in self play?
RosentWinternet_502_24x64 *diffN=40000Elo: 2.86 +- 7.28 (95%) [N=4000]
Games: 4006 W: 1111 L: 1078 D: 1817
Ptnml(0-2): 102, 478, 824, 483, 116
Get an understanding of how far apart the based increased size net is compared to the current version. Fixed nodes in this case as system load would otherwise distort results.
RosentWinternet_502_16x96 *diff10.0+0.10LLR: -2.95 (-2.94, 2.94) [-5.00, 0.00]
Games: 8948 W: 1999 L: 2170 D: 4779
Ptnml(0-2): 186, 1157, 1942, 1020, 169
The larger 24x64 net architecture is failing STC so we instead try 16x96. This has far fewer parameters but still has a 5% slowdown we need to overcome. Based on training metrics this lies squarely between the baseline and 24x64.
RosentWinternet_502_24x64 *diff10.0+0.10LLR: -2.95 (-2.94, 2.94) [-5.00, 0.00]
Games: 1566 W: 309 L: 453 D: 804
Ptnml(0-2): 65, 210, 333, 154, 21
Having passed the fixed node test as expected, we try an STC test with [-5, 0] bounds, as the slowdown is expected to hurt more than in actual play.
RosentWinternet_502_24x64 *diffN=40000LLR: 2.99 (-2.94, 2.94) [0.00, 5.00]
Games: 2282 W: 745 L: 582 D: 955
Ptnml(0-2): 69, 209, 455, 306, 102
Larger net, hopefully a clear improvement at fixed nodes. Around 16% less Nps.
RosentWinternet_311m *diff8.0+0.08LLR: -3.02 (-2.94, 2.94) [-2.50, 2.50]
Games: 17246 W: 4156 L: 4292 D: 8798
Ptnml(0-2): 402, 2127, 3657, 2079, 358
Trying to better understand the hybrid loss. Both nets are not fully trained, but are at a similar point in training.
RosentWinternet_311l *diff8.0+0.08LLR: -3.10 (-2.94, 2.94) [-3.00, 2.00]
Games: 17742 W: 4218 L: 4374 D: 9150
Ptnml(0-2): 424, 2152, 3826, 2094, 375
Trying to better understand the hybrid loss. Both nets are not fully trained, but are at a similar point in training. 311n removes the CE loss component completely. Even if worth Elo, we may not want this.
RosentWinternet_311l *diff8.0+0.08LLR: -2.97 (-2.94, 2.94) [0.00, 5.00]
Games: 23074 W: 5479 L: 5505 D: 12090
Ptnml(0-2): 487, 2821, 4936, 2817, 476
Final net with less CE loss component.
RosentWinternet_311d *diff8.0+0.08LLR: -2.95 (-2.94, 2.94) [0.00, 5.00]
Games: 20226 W: 5005 L: 5044 D: 10177
Ptnml(0-2): 475, 2478, 4222, 2487, 451
Reduced the weighting of the draw prediction loss component.
RosentWinternet_311d *diff8.0+0.08LLR: -2.95 (-2.94, 2.94) [0.00, 5.00]
Games: 11346 W: 2730 L: 2809 D: 5807
Ptnml(0-2): 236, 1434, 2411, 1357, 235
Draw calibration handled with MSE loss directly on the draw probability should be less sensitive to outliers that are common in the high entropy STC time controls the training data stems from.
RosentWinternet_311 *diff40.0+0.40LLR: 2.96 (-2.94, 2.94) [0.00, 5.00]
Games: 3948 W: 860 L: 731 D: 2357
Ptnml(0-2): 32, 395, 1004, 498, 45
Fully trained model with identical hyperparameters to prior gen, but extra data. This should clearly beat the master branch.
RosentWinternet_311 *diff8.0+0.08LLR: 2.96 (-2.94, 2.94) [0.00, 5.00]
Games: 8158 W: 2101 L: 1925 D: 4132
Ptnml(0-2): 193, 922, 1694, 1056, 214
Testing net with same hyperparameters but extra self play data a bit prematurely to get a sense of where things lie.
RosentWinternet_test *diff40.0+0.40LLR: 2.95 (-2.94, 2.94) [0.00, 5.00]
Games: 3618 W: 807 L: 675 D: 2136
Ptnml(0-2): 37, 366, 880, 480, 46
Trained with new data. The new training data has a very different distribution and has neutral effect on validation metrics.
RosentWinternet_test *diff8.0+0.08LLR: 2.97 (-2.94, 2.94) [-2.00, 3.00]
Games: 7204 W: 1851 L: 1710 D: 3643
Ptnml(0-2): 160, 820, 1495, 973, 154
Trained with new data. The new training data has a very different distribution and has neutral effect on validation metrics.
RosentWinternet_test *diff40.0+0.40LLR: 1.77 (-2.94, 2.94) [0.00, 5.00]
Games: 4820 W: 1009 L: 922 D: 2889
Ptnml(0-2): 42, 513, 1225, 576, 54
Net trained with some extra data I had laying around and not yet utilized.
RosentWinternet_test *diff8.0+0.08LLR: 2.95 (-2.94, 2.94) [0.00, 5.00]
Games: 4334 W: 1208 L: 1050 D: 2076
Ptnml(0-2): 92, 482, 903, 556, 134
Net trained with some extra data I had laying around and not yet utilized.
RosentWinternet_test *diff40.0+0.40LLR: 2.17 (-2.94, 2.94) [0.00, 5.00]
Games: 8822 W: 1791 L: 1673 D: 5358
Ptnml(0-2): 80, 938, 2276, 1018, 99
Confirmed 2 year old net reproduced and slightly improved
RosentWinternet_test *diff8.0+0.08LLR: 3.03 (-2.94, 2.94) [0.00, 5.00]
Games: 27442 W: 6402 L: 6150 D: 14890
Ptnml(0-2): 464, 3213, 6148, 3399, 497
RosentWinternet_test *diff8.0+0.08LLR: -3.11 (-2.94, 2.94) [0.00, 5.00]
Games: 4948 W: 1188 L: 1304 D: 2456
Ptnml(0-2): 109, 648, 1070, 544, 103
RosentWinternet_test *diff8.0+0.08LLR: -3.06 (-2.94, 2.94) [0.00, 5.00]
Games: 7424 W: 1778 L: 1880 D: 3766
Ptnml(0-2): 163, 943, 1583, 879, 144
RosentWinternet_test *diff8.0+0.08LLR: -3.05 (-2.94, 2.94) [0.00, 5.00]
Games: 4774 W: 1109 L: 1224 D: 2441
Ptnml(0-2): 124, 593, 1040, 534, 96
RosentWinternet_test *diff8.0+0.08LLR: -3.02 (-2.94, 2.94) [0.00, 5.00]
Games: 6534 W: 1565 L: 1671 D: 3298
Ptnml(0-2): 155, 823, 1413, 725, 151
RosentWinterfable_bugs *diff8.0+0.08LLR: -2.97 (-2.94, 2.94) [-5.00, 0.00]
Games: 48452 W: 11655 L: 12015 D: 24782
Ptnml(0-2): 1111, 6015, 10255, 5813, 1032
RosentWinterminor_performance_improvementsdiff40.0+0.40Elo: 77.61 +- 5.83 (95%) [N=4000]
Games: 4000 W: 1121 L: 242 D: 2637
Ptnml(0-2): 2, 142, 963, 761, 132
RosentWinterminor_performance_improvements *diff8.0+0.08LLR: 3.00 (-2.94, 2.94) [0.00, 5.00]
Games: 1760 W: 516 L: 372 D: 872
Ptnml(0-2): 23, 183, 351, 273, 50