Search is not available for this dataset
seed int64 88.4M 2.02B | floor_prob float64 0 0.2 | train_rewards listlengths 800 800 | late_train_mean float64 0 0.2 | late_train_std float64 0 1.24 | eval_rewards listlengths 5 5 | eval_mean float64 0 0.86 | eval_std float64 0 1.71 | train_time_s int64 6.73k 52.6k | total_time_s int64 6.74k 52.6k |
|---|---|---|---|---|---|---|---|---|---|
88,409,749 | 0 | [
0,
0,
0,
0.14,
0,
2.71,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,256 | 8,298 |
88,409,749 | 0.2 | [
1.8599999999999999,
0,
0.29,
0,
0.29,
0,
0,
0,
0.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.06 | 0.4 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,171 | 8,211 |
242,285,876 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.71,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.1... | 0.05 | 0.38 | [
0,
0,
0,
0,
0.14
] | 0.03 | 0.06 | 8,279 | 8,321 |
242,285,876 | 0.2 | [
1,
0,
0,
0,
0,
0,
0,
1.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
2.71,
0,
0,
0,
0,
2.71,... | 0 | 0 | [
1.57,
0,
0,
0,
0
] | 0.31 | 0.63 | 7,990 | 8,029 |
249,467,210 | 0 | [
0,
1.1400000000000001,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 9,985 | 10,032 |
249,467,210 | 0.2 | [
0,
0,
0,
0,
0,
0,
2.29,
0,
0,
0,
0,
0,
0,
0,
1.29,
0,
0,
3.71,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 10,100 | 10,142 |
415,968,276 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.86,
0,
0.14,
0,
0,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
6.71,
0... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,737 | 6,747 |
415,968,276 | 0.2 | [
0.14,
0.86,
0,
0,
0,
0,
0.5700000000000001,
0,
0,
0,
0,
1.1400000000000001,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.86,
0,
0,
0.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,771 | 6,781 |
423,734,972 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
1.71,
0,
0,
0,
1.57,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,744 | 6,754 |
423,734,972 | 0.2 | [
0,
0,
0,
0,
0,
0,
0,
5,
0,
0,
2.86,
0,
0,
0.29,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0.5700000000000001,
0,
1.1400000000000001,
11,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.05 | 0.25 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,747 | 6,757 |
429,389,014 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
8.29,
8.29,
0,
8.71,
0,
0,
0,
0,
0,
0,
0,
0,... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 52,636 | 52,645 |
429,389,014 | 0.2 | [
0,
0,
0,
0.5700000000000001,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
3.29,
10.86,
0,
0,
4.43,
0,
0,
0,
0,
0.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | 0.1 | 0.42 | [
0,
0,
0,
0,
0
] | 0 | 0 | 12,116 | 12,127 |
434,285,667 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
4.14,
0.43,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.43,
2.5700000000000003,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,158 | 8,197 |
434,285,667 | 0.2 | [
0,
0,
0,
2.29,
0.14,
1.43,
0,
0,
0.43,
2.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
3.29,
0,
4.86,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.02 | 0.1 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,144 | 8,185 |
613,608,295 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,200 | 8,241 |
613,608,295 | 0.2 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
3,
0,
0,
0,
4.14,
0,
0,
5,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.01 | 0.1 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,172 | 8,213 |
648,061,058 | 0 | [
0,
0,
0,
0,
0,
0,
0.5700000000000001,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,238 | 8,276 |
648,061,058 | 0.2 | [
0,
2,
0,
0,
0,
0,
0,
0,
0,
6.43,
13,
10,
5.14,
0,
0,
0,
0,
4.29,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
4.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 7,926 | 7,967 |
669,991,378 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.86,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,726 | 6,735 |
669,991,378 | 0.2 | [
0,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
1.71,
0,
0,
0,
0,
0,
0.43,
0,
0,
0.43,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 40,453 | 40,462 |
670,094,950 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | 0.2 | 0.99 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,747 | 6,757 |
670,094,950 | 0.2 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.86,
0,
0,
0,
0.86,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,... | 0.18 | 1.24 | [
0,
0,
0,
4.29,
0
] | 0.86 | 1.71 | 6,744 | 6,753 |
787,846,414 | 0 | [
0,
0,
0,
0,
0,
0,
0,
6.29,
0,
0,
0,
0,
0,
0.43,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.14 | 0.86 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,784 | 6,794 |
787,846,414 | 0.2 | [
0,
0,
0,
0,
0.71,
2.43,
3.71,
0,
0.86,
0,
2.43,
0,
0,
0,
0,
0,
0,
0.5700000000000001,
0,
0,
0,
0.43,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
17.43,
0,
0,
0,
19.29,
... | 0.03 | 0.24 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,789 | 6,800 |
893,664,919 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
2.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,162 | 8,207 |
893,664,919 | 0.2 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,047 | 8,087 |
953,477,463 | 0 | [
0,
0,
0,
0,
0,
0,
0.43,
0,
0,
3.29,
0,
6.43,
2.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.01 | 0.04 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,100 | 8,143 |
953,477,463 | 0.2 | [
0,
0,
0.29,
0,
0,
0,
0.43,
0.5700000000000001,
21.14,
2.71,
15,
0.43,
5,
11,
0,
0,
4.29,
3.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.01 | 0.04 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,388 | 8,434 |
996,406,378 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,822 | 6,832 |
996,406,378 | 0.2 | [
0,
1.29,
0,
0,
0,
0,
0,
0,
0,
0,
0.43,
0,
0,
0,
1.29,
0,
0,
0,
0,
0,
1.57,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,792 | 6,802 |
1,201,263,687 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | 0.01 | 0.08 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,792 | 6,802 |
1,201,263,687 | 0.2 | [
0.43,
3.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.71,
0,
0,... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,812 | 6,822 |
1,273,642,419 | 0 | [
0,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,796 | 6,806 |
1,273,642,419 | 0.2 | [
0,
0,
0,
0,
1.29,
0,
0,
0,
0,
0,
0.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
2,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
5.29,
0,
0.14,
0,
0,
1.8599999999999999,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
... | 0.01 | 0.06 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,779 | 6,789 |
1,427,830,251 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
3.29,
0,
0,
0,
4.43,
3.86,
0,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.14,
0,
0,
0,
2.43,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,627 | 8,673 |
1,427,830,251 | 0.2 | [
0,
0,
0,
0,
0,
0.5700000000000001,
0,
4.57,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.43,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.17 | 0.85 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,445 | 8,488 |
1,433,267,572 | 0 | [
0,
0,
0,
0,
0,
0,
0,
4.14,
0,
0.71,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,167 | 8,208 |
1,433,267,572 | 0.2 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
2,
0,
4.43,
0.43,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1.57,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,... | 0 | 0.02 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,188 | 8,229 |
1,572,714,583 | 0 | [
0,
0,
0,
0,
0,
0,
3.86,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,181 | 8,224 |
1,572,714,583 | 0.2 | [
2.5700000000000003,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | 0.1 | 0.49 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,108 | 8,149 |
1,608,637,542 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.08 | 0.46 | [
0,
0,
0,
0,
0
] | 0 | 0 | 17,492 | 18,401 |
1,608,637,542 | 0.2 | [
0,
0.29,
0,
10.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
6.57,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0... | 0 | 0 | [
0,
0,
0,
0.29,
0
] | 0.06 | 0.11 | 9,349 | 9,359 |
1,914,837,113 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
7.86,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,758 | 6,767 |
1,914,837,113 | 0.2 | [
0,
0,
0,
0,
2.43,
0,
0.29,
0.29,
0,
2.14,
5.71,
0,
0.14,
4.86,
2.71,
4.43,
0,
0,
0,
12.86,
0,
1.1400000000000001,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.11 | 0.76 | [
0,
0,
0,
1.29,
0
] | 0.26 | 0.51 | 6,728 | 6,738 |
1,935,803,228 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.43,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
8.86,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,776 | 6,786 |
1,935,803,228 | 0.2 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
5,
3.14,
0,
0,
3.5700000000000003,
13.43,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 6,816 | 6,826 |
1,972,458,954 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.14,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.05 | 0.27 | [
0,
0,
0,
0,
0
] | 0 | 0 | 9,995 | 10,039 |
1,972,458,954 | 0.2 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1.43,
0,
0,
0,
0,
0,
3.43,
10,
5.86,
5.57,
0,
0,
0,
0,
... | 0.03 | 0.2 | [
0,
0,
0,
0,
0
] | 0 | 0 | 9,939 | 9,991 |
2,018,247,425 | 0 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.29,
0,
0,
0,
0,
0.29,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.5700000000000001,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
6.71,
0,
0,
0,
0,
0,
0,
0,
... | 0 | 0 | [
0,
0,
0,
0,
0
] | 0 | 0 | 7,893 | 7,934 |
2,018,247,425 | 0.2 | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.14,
0,
4,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
... | 0.07 | 0.3 | [
0,
0,
0,
0,
0
] | 0 | 0 | 8,382 | 8,421 |
🛈 Anonymity Notice (2026-05-12): The associated manuscript is currently under peer review at a double-blind venue. Author identity and venue-specific identifiers have been withheld throughout this README, the BibTeX templates, and the CITATION.cff block. The dataset itself remains CC-BY-4.0 and is independently citable via its Zenodo DOI
10.5281/zenodo.20018466. The author byline will be restored after the review outcome is announced.
DOI
This dataset is citable via DataCite DOI 10.5281/zenodo.20018466 (Zenodo record).
Cite as:
@dataset{neogenesis_20018466,
author = {{Anonymous (under peer review)}},
title = {EthicaAI Mixed-Safe Multi-Environment Evidence Dataset},
year = 2026,
publisher = {Zenodo},
doi = {10.5281/zenodo.20018466},
url = {https://doi.org/10.5281/zenodo.20018466}
}
EthicaAI Mixed-Safe Multi-Environment Evidence Dataset
Underlying experimental evidence from Neo Genesis research on multi-agent cooperative-constraint learning — released as an open dataset for community replication and AI safety research. The associated manuscript is currently under peer review at a double-blind venue; author identity and venue-specific identifiers are withheld until review outcome is announced.
What this dataset is
A 3-environment evidence bundle showing how multi-agent reinforcement-learning agents behave under different cooperative-constraint regimes. Each environment was deliberately chosen to stress-test different aspects of the mixed-safe boundary thesis:
| Environment | What it tests | Sample size | Key statistic |
|---|---|---|---|
Melting Pot (clean_up) |
Boundary-consistent cooperative learning under floor-probability sweep vs. baseline | 50 seed × floor_prob runs | t-test eval: floor_mean = 0.06, baseline_mean = 0.001, p = 0.063502 |
| Coin Game Deep (adapted) | Selfish equilibrium vs. MACCL (Multi-Agent Cooperative Constraint Learning) on 160 seeds | 160 seeds × 200 episodes | MACCL survival ≫ selfish survival (paper-level finding) |
| Fishery Nash Trap | Ecological tragedy-of-commons with 300 seeds and tipping-point dynamics | 300 seeds × 300 episodes | survival vs. harvest welfare frontier |
The three environments together form the mixed-safe corpus — the paper's central claim is that no single environment proves the thesis, but the boundary consistency across three independently-designed substrates does.
Why publish the data
Most AI-safety papers expose plots and aggregate statistics but withhold seed-level results. This dataset releases:
- All 50 seed-level Melting Pot runs with full
train_rewardstrajectories per seed - The exact
late_trainandevalstatistics used in the paper's t-tests, p-values, bootstrap CIs - Coin Game
selfishvsmacclaggregated per-seed metrics - Fishery 300-seed survival/welfare results across
phi1calibration values - Source-file provenance (which results came from which compute node — Mac Studio head shard vs. Linux server tail shard)
This lets independent researchers:
- Replicate the paper's claims by re-running the same seeds
- Extend the analysis with their own cooperative-constraint variants
- Use the seed-level data as a benchmark for new MARL methods
Files
data/
├── meltingpot_results.jsonl 50 lines, one per (seed, floor_prob) combination
├── meltingpot_statistics.json paper-grade t-test + bootstrap CI per condition
├── coin_game_deep.json selfish vs maccl, 160 seeds × 200 episodes
├── fishery_default.json default ecological config
└── fishery_300seed.json 300-seed phi1 sweep, 300 episodes per seed
Schema (meltingpot_results.jsonl)
Each line is one (seed, floor_prob) run:
{
"seed": 88409749,
"floor_prob": 0.0,
"train_rewards": [0.0, 0.0, 0.14, 2.71, ...],
"eval_rewards": [...]
}
train_rewards is the per-episode reward time series during training; eval_rewards is the held-out evaluation reward. floor_prob = 0.0 corresponds to baseline (no cooperative floor); floor_prob > 0 corresponds to mixed-safe condition.
Quick start
from datasets import load_dataset
ds = load_dataset("neogenesislab/ethicaai-mixed-safe-evidence", split="train", data_files="data/meltingpot_results.jsonl")
print(len(ds), "Melting Pot runs")
# Per-condition statistics (already computed):
import json, urllib.request
url = "https://huggingface.co/datasets/neogenesislab/ethicaai-mixed-safe-evidence/resolve/main/data/meltingpot_statistics.json"
stats = json.loads(urllib.request.urlopen(url).read())
print("eval floor_mean:", stats["statistics"]["eval"]["floor_mean"])
print("eval baseline_mean:", stats["statistics"]["eval"]["baseline_mean"])
print("p-value:", stats["statistics"]["eval"]["p_value"])
Reproducing the paper's findings
The full reproduction code (RLlib + Melting Pot harness + Coin Game adapter + Fishery simulator) is referenced in the paper's appendix. The dataset above is the frozen evidence snapshot used in the camera-ready manuscript.
Statistical machinery (provided in meltingpot_statistics.json):
- Welch's t-test (unequal variance) for floor vs baseline mean reward
- Bootstrap 95% CI on the difference (resamples documented in
config) - Cohen's d effect size
- Per-shard provenance for distributed compute audit
Related Neo Genesis assets
- 📄 Research entry — see
https://neogenesis.app/data/research/ethicaai-melting-pot-mixed-safe(manuscript under double-blind peer review) - 🤗 Companion dataset —
neogenesislab/korean-rag-ssot-golden-50 - 🆔 Wikidata — Q139569718 (EthicaAI) · Q139569680 (Neo Genesis parent org)
- 🌐 Org site — https://neogenesis.app
Citation
@misc{ethicaai_mixed_safe_evidence_2026_anon,
title = {EthicaAI Mixed-Safe Multi-Environment Evidence Dataset},
author = {{Anonymous (under peer review)}},
howpublished = {Hugging Face Datasets},
year = {2026},
url = {https://huggingface.co/datasets/neogenesislab/ethicaai-mixed-safe-evidence},
note = {Underlying evidence dataset, CC-BY-4.0}
}
License
CC-BY-4.0. Free for research and commercial use with attribution to Neo Genesis and the underlying paper.
Provenance
- Compute nodes: Mac Studio (head shard
seed_indices 0-23) + Linux server (tail shardseed_indices 24-49) — seemeltingpot_statistics.jsonsource_summary - Curation: Neo Genesis Research (manuscript under peer review; curator identity withheld pending outcome)
- Frozen 2026-04-15 (
meltingpot_n80_stats.jsonpaper-anchor revision) - Released 2026-04-28
Citation
@dataset{neogenesislab_ethicaai_mixed_safe_evidence_2026,
author = {{Anonymous (under peer review)}},
title = {EthicaAI Mixed-Safe Cooperation Multi-Environment Evidence Dataset 2026},
year = 2026,
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/neogenesislab/ethicaai-mixed-safe-evidence},
note = {Wikidata Q139569680, Q139569708; license CC-BY-4.0}
}
Citation File Format
GitHub, Zenodo, and other tooling can read the following CFF block to provide one-click citation export (BibTeX, APA, RIS, etc.). The CFF specification is v1.2.0.
cff-version: 1.2.0
message: "If you use this dataset, please cite it as below."
title: "EthicaAI Mixed-Safe Multi-Environment Evidence Dataset"
type: dataset
authors:
- name: "Anonymous (under peer review)"
affiliation: "Neo Genesis Research"
date-released: "2026-04-28"
license: CC-BY-4.0
url: "https://huggingface.co/datasets/neogenesislab/ethicaai-mixed-safe-evidence"
repository: "https://huggingface.co/datasets/neogenesislab/ethicaai-mixed-safe-evidence"
identifiers:
- type: doi
value: "10.5281/zenodo.20018466"
description: "Zenodo DataCite DOI for this dataset"
- type: other
value: "Q139569680"
description: "Wikidata Q-ID of the publishing organization (Neo Genesis)"
keywords:
- multi-agent
- alignment
- constitutional-ai
- melting-pot
- coin-game
- fishery
- ethicaai
- neo-genesis
preferred-citation:
type: dataset
title: "EthicaAI Mixed-Safe Multi-Environment Evidence Dataset"
authors:
- name: "Anonymous (under peer review)"
affiliation: "Neo Genesis Research"
doi: "10.5281/zenodo.20018466"
year: 2026
publisher:
name: "Zenodo"
url: "https://doi.org/10.5281/zenodo.20018466"
- Downloads last month
- 66