run_id stringlengths 12 22 | fault_kind stringclasses 6
values | fault_description stringclasses 6
values | expected_behaviour stringclasses 6
values | first_bad_step int64 540 900 | n_steps int64 1.2k 1.2k | seed int64 0 2 | preset stringclasses 1
value | param_count int64 534k 534k | batch int64 8 8 | lr float64 0 0 | sketch_bytes_per_step int64 452 452 | loss_only_prediction int64 -1 901 | loss_only_error int64 -1 1 | flashback_prediction int64 540 900 | flashback_error int64 0 0 | flashback_metric stringclasses 7
values | flashback_votes int64 4 86 | final_loss float64 1.06 4.25 ⌀ | split stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
lr_spike_loud__s1 | lr_spike_loud | learning rate x60 for one step | loud: the loss spikes within a step or two, so the baseline should also find it | 720 | 1,200 | 1 | tiny | 533,760 | 8 | 0.003 | 452 | 721 | 1 | 720 | 0 | update_norm | 61 | 1.062434 | train |
lr_spike_loud__s2 | lr_spike_loud | learning rate x60 for one step | loud: the loss spikes within a step or two, so the baseline should also find it | 900 | 1,200 | 2 | tiny | 533,760 | 8 | 0.003 | 452 | 901 | 1 | 900 | 0 | group:attn_qkv:upd_param_ratio | 43 | 1.126062 | train |
lr_spike_subtle__s1 | lr_spike_subtle | learning rate x4 sustained to the end of the run | slow: the loss degrades gradually, so 'when did it start' is genuinely hard by eye | 720 | 1,200 | 1 | tiny | 533,760 | 8 | 0.003 | 452 | -1 | -1 | 720 | 0 | update_param_ratio | 25 | 1.140905 | train |
lr_spike_subtle__s2 | lr_spike_subtle | learning rate x4 sustained to the end of the run | slow: the loss degrades gradually, so 'when did it start' is genuinely hard by eye | 900 | 1,200 | 2 | tiny | 533,760 | 8 | 0.003 | 452 | -1 | -1 | 900 | 0 | hist_update:9 | 24 | 1.214551 | train |
bit_flip__s0 | bit_flip | one flipped exponent bit in one gradient element (silent data corruption) | silent: Adam normalises the update away, so the loss curve often never reacts | 540 | 1,200 | 0 | tiny | 533,760 | 8 | 0.003 | 452 | -1 | -1 | 540 | 0 | grad_norm | 15 | 1.111216 | train |
bit_flip__s1 | bit_flip | one flipped exponent bit in one gradient element (silent data corruption) | silent: Adam normalises the update away, so the loss curve often never reacts | 720 | 1,200 | 1 | tiny | 533,760 | 8 | 0.003 | 452 | -1 | -1 | 720 | 0 | grad_norm | 15 | 1.059853 | train |
lowprec_overflow__s0 | lowprec_overflow | fp8-range overflow in the gradients -> Inf/NaN | abrupt: non-finite counters fire on the exact step | 540 | 1,200 | 0 | tiny | 533,760 | 8 | 0.003 | 452 | 541 | 1 | 540 | 0 | grad_norm | 78 | null | train |
lowprec_overflow__s2 | lowprec_overflow | fp8-range overflow in the gradients -> Inf/NaN | abrupt: non-finite counters fire on the exact step | 900 | 1,200 | 2 | tiny | 533,760 | 8 | 0.003 | 452 | 901 | 1 | 900 | 0 | grad_norm | 86 | null | train |
data_poison_heavy__s0 | data_poison_heavy | 50% of every batch replaced by unlearnable tokens | the loss moves too, but gradient-direction statistics move first | 540 | 1,200 | 0 | tiny | 533,760 | 8 | 0.003 | 452 | 540 | 0 | 540 | 0 | loss | 60 | 4.245008 | train |
data_poison_heavy__s1 | data_poison_heavy | 50% of every batch replaced by unlearnable tokens | the loss moves too, but gradient-direction statistics move first | 720 | 1,200 | 1 | tiny | 533,760 | 8 | 0.003 | 452 | 720 | 0 | 720 | 0 | grad_mean | 45 | 4.180503 | train |
data_poison_subtle__s0 | data_poison_subtle | 12% of every batch replaced by unlearnable tokens | hard: a small contamination is inside the loss curve's own noise band | 540 | 1,200 | 0 | tiny | 533,760 | 8 | 0.003 | 452 | 540 | 0 | 540 | 0 | loss | 4 | 1.916396 | train |
data_poison_subtle__s1 | data_poison_subtle | 12% of every batch replaced by unlearnable tokens | hard: a small contamination is inside the loss curve's own noise band | 720 | 1,200 | 1 | tiny | 533,760 | 8 | 0.003 | 452 | 720 | 0 | 720 | 0 | grad_mean | 4 | 1.869771 | train |
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