viciousa3gis/hypodiverse-grpo
Text Generation • 4B • Updated • 157
agent_name stringclasses 1
value | data_source stringclasses 1
value | env_spec_json stringlengths 1.9k 3.26k | extra_info dict | index int64 0 6.14k | prompt stringlengths 884 1.37k | raw_prompt stringlengths 884 1.37k | reward_model dict | state_json stringlengths 1.47k 2.83k |
|---|---|---|---|---|---|---|---|---|
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 0, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward":... | {
"index": 0,
"max_global_steps": 0,
"min_global_steps": 0
} | 0 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "mixed", "maximum_separation": 0.22857142857142856, "mean_separation": 0.15238095238095237, "minimum_separat... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 1, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward":... | {
"index": 1,
"max_global_steps": 0,
"min_global_steps": 0
} | 1 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], "metadata": {"evidence_size": 7, "family_bucket": "mixed", "maximum_separation": 0.42424242424242425, "mean_separation": 0.25252525252525254, "minimum_separation":... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 2, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward":... | {
"index": 2,
"max_global_steps": 0,
"min_global_steps": 0
} | 2 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [0, 1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "mixed", "maximum_separation": 0.45714285714285713, "mean_separation": 0.27619047619047615, "minimum_separ... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 3, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward":... | {
"index": 3,
"max_global_steps": 0,
"min_global_steps": 0
} | 3 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 6, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mea... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 4, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward":... | {
"index": 4,
"max_global_steps": 0,
"min_global_steps": 0
} | 4 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 6, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_me... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 5, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward":... | {
"index": 5,
"max_global_steps": 0,
"min_global_steps": 0
} | 5 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], "metadata": {"evidence_size": 8, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mean_sep... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 6, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward":... | {
"index": 6,
"max_global_steps": 0,
"min_global_steps": 0
} | 6 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "within_family", "maximum_separation": 0.17142857142857143, "mean_separation": 0.1714285714285714, "minimu... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 7, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward":... | {
"index": 7,
"max_global_steps": 0,
"min_global_steps": 0
} | 7 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 8, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward":... | {
"index": 8,
"max_global_steps": 0,
"min_global_steps": 0
} | 8 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39], "metadata": {"evidence_size": 6, "family_bucket": "mixed", "maximum_separation": 0.4117647058823529, "mean_separation": 0.24509803921568626, "minimum_separation... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 9, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward":... | {
"index": 9,
"max_global_steps": 0,
"min_global_steps": 0
} | 9 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 10, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 10,
"max_global_steps": 0,
"min_global_steps": 0
} | 10 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 7, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mean_s... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 11, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 11,
"max_global_steps": 0,
"min_global_steps": 0
} | 11 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 6, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mea... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 12, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 12,
"max_global_steps": 0,
"min_global_steps": 0
} | 12 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_m... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 13, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 13,
"max_global_steps": 0,
"min_global_steps": 0
} | 13 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_m... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 14, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 14,
"max_global_steps": 0,
"min_global_steps": 0
} | 14 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 7, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mean_... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 15, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 15,
"max_global_steps": 0,
"min_global_steps": 0
} | 15 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 7, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mean_s... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 16, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 16,
"max_global_steps": 0,
"min_global_steps": 0
} | 16 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 4, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normali... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 17, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 17,
"max_global_steps": 0,
"min_global_steps": 0
} | 17 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [2, 3, 4, 6, 7, 8, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 6, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_me... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 18, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 18,
"max_global_steps": 0,
"min_global_steps": 0
} | 18 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [0, 1, 2, 3, 4, 6, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "cross_family", "maximum_separation": 0.4, "mean_separation": 0.2857142857142857, "minimum_separation": 0.1... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 19, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 19,
"max_global_steps": 0,
"min_global_steps": 0
} | 19 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39], "metadata": {"evidence_size": 6, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mea... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 20, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 20,
"max_global_steps": 0,
"min_global_steps": 0
} | 20 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 21, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 21,
"max_global_steps": 0,
"min_global_steps": 0
} | 21 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34, 36, 37, 38, 39], "metadata": {"evidence_size": 6, "family_bucket": "mixed", "maximum_separation": 0.5882352941176471, "mean_separation": 0.4705882352941177, "minimum_separation"... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 22, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 22,
"max_global_steps": 0,
"min_global_steps": 0
} | 22 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39], "metadata": {"evidence_size": 7, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mean_s... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 23, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 23,
"max_global_steps": 0,
"min_global_steps": 0
} | 23 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "within_family", "maximum_separation": 0.34285714285714286, "mean_separation": 0.28571428571428575, "minimu... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 24, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 24,
"max_global_steps": 0,
"min_global_steps": 0
} | 24 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 4, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalize... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 25, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 25,
"max_global_steps": 0,
"min_global_steps": 0
} | 25 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 7, "family_bucket": "cross_family", "maximum_separation": 0.42424242424242425, "mean_separation": 0.30303030303030304, "minimum_separ... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 26, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 26,
"max_global_steps": 0,
"min_global_steps": 0
} | 26 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], "metadata": {"evidence_size": 6, "family_bucket": "mixed", "maximum_separation": 0.4117647058823529, "mean_separation": 0.24509803921568626, "minimum_separation... |
causal_micro_lab_agent_loop | causal_micro_lab_train | {"agent": {"length_penalty_max": -0.2, "length_penalty_start": 3072.0, "mask_truncated": true}, "env_type": "causal_micro_lab", "max_commit": 1, "max_consecutive_invalid": 2, "max_steps": 1, "protocol": "single", "seed": 27, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 27,
"max_global_steps": 0,
"min_global_steps": 0
} | 27 | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | Infer one Boolean causal program consistent with the evidence.
Variables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.
Rules: exactly one shallow rule for each target Z1, Z2, Y.
Operators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).
Available inputs:
- Z1 rule can use: X1, X2, X3
- Z2 rule can use: X1, X2, X3, Z1... | {
"style": "rule"
} | {"available_experiment_ids": [3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 7, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mean_... |
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HypoDiverse
This release contains the exact JSONL rows used to train and evaluate the HypoDiverse models. HypoDiverse is an enumerable synthetic benchmark for measuring the validity, uniqueness, repetition, and predictive diversity of sets of scientific hypotheses. The release tool copies the frozen files and never regenerates examples during publishing.
Splits
| Split | Rows | Provenance |
|---|---|---|
| train | 6144 | Exact verl_train.jsonl used by GRPO and LIFPO |
| validation | 128 | Exact verl_val.jsonl used during training |
| test | 192 | Frozen final_v3/verl_test.jsonl evaluation set |
The files under data/ can be loaded with datasets.load_dataset("json", data_files=...). Each row retains the veRL-compatible prompt, environment
state, and verifier metadata required by the original training or evaluation
pipeline. Source tables, state files, and manifests are preserved under
source/ when they exist. Exact run and evaluation configurations are under
provenance/configs/.
release_manifest.json records row counts, SHA256 hashes, source paths, Git
provenance, and state-ID/prompt overlap checks. The manifest intentionally does
not hash itself; every other packaged file is hashed there.
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