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_... |
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": 28, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 28,
"max_global_steps": 0,
"min_global_steps": 0
} | 28 | 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 24, 25, 26, 27, 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": 29, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 29,
"max_global_steps": 0,
"min_global_steps": 0
} | 29 | 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, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 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": 30, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 30,
"max_global_steps": 0,
"min_global_steps": 0
} | 30 | 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, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 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": 31, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 31,
"max_global_steps": 0,
"min_global_steps": 0
} | 31 | 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, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "mixed", "maximum_separation": 0.4, "mean_separation": 0.2380952380952381, "minimum_separation": 0.0, "nor... |
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": 32, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 32,
"max_global_steps": 0,
"min_global_steps": 0
} | 32 | 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, 6, 7, 8, 9, 10, 11, 12, 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": 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": 33, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 33,
"max_global_steps": 0,
"min_global_steps": 0
} | 33 | 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, 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, 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": 34, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 34,
"max_global_steps": 0,
"min_global_steps": 0
} | 34 | 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, 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... |
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": 35, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 35,
"max_global_steps": 0,
"min_global_steps": 0
} | 35 | 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, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 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": 36, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 36,
"max_global_steps": 0,
"min_global_steps": 0
} | 36 | 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, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "within_family", "maximum_separation": 0.5714285714285714, "mean_separation": 0.41904761904761906, "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": 37, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 37,
"max_global_steps": 0,
"min_global_steps": 0
} | 37 | 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": [5, 6, 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, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 8, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mean_sepa... |
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": 38, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 38,
"max_global_steps": 0,
"min_global_steps": 0
} | 38 | 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, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 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": 39, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 39,
"max_global_steps": 0,
"min_global_steps": 0
} | 39 | 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, 19, 20, 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": 40, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 40,
"max_global_steps": 0,
"min_global_steps": 0
} | 40 | 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, 2, 3, 4, 6, 7, 8, 9, 11, 12, 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": 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": 41, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 41,
"max_global_steps": 0,
"min_global_steps": 0
} | 41 | 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, 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, 36, 37, 38, 39], "metadata": {"evidence_size": 4, "family_bucket": "mixed", "maximum_separation": 0.2222222222222222, "mean_separation": 0.14814814814814814, "minimum_sepa... |
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": 42, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 42,
"max_global_steps": 0,
"min_global_steps": 0
} | 42 | 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 36, 37, 38, 39], "metadata": {"evidence_size": 6, "family_bucket": "within_family", "maximum_separation": 0.35294117647058826, "mean_separation": 0.2647058823529412, "minimum_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": 43, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 43,
"max_global_steps": 0,
"min_global_steps": 0
} | 43 | 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, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 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_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": 44, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 44,
"max_global_steps": 0,
"min_global_steps": 0
} | 44 | 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, 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, 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": 45, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 45,
"max_global_steps": 0,
"min_global_steps": 0
} | 45 | 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, 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": 46, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 46,
"max_global_steps": 0,
"min_global_steps": 0
} | 46 | 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 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_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": 47, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 47,
"max_global_steps": 0,
"min_global_steps": 0
} | 47 | 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, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 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_separa... |
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": 48, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 48,
"max_global_steps": 0,
"min_global_steps": 0
} | 48 | 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, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 8, "family_bucket": "cross_family", "maximum_separation": 0.4375, "mean_separation": 0.3125, "minimum_separation": 0.125, "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": 49, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 49,
"max_global_steps": 0,
"min_global_steps": 0
} | 49 | 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, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38], "metadata": {"evidence_size": 5, "family_bucket": "mixed", "maximum_separation": 0.4, "mean_separation": 0.2380952380952381, "minimum_separation": 0.0, "norm... |
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": 50, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 50,
"max_global_steps": 0,
"min_global_steps": 0
} | 50 | 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, 19, 20, 21, 22, 23, 24, 26, 27, 29, 30, 31, 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": 51, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 51,
"max_global_steps": 0,
"min_global_steps": 0
} | 51 | 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 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": 52, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 52,
"max_global_steps": 0,
"min_global_steps": 0
} | 52 | 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, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 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_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": 53, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 53,
"max_global_steps": 0,
"min_global_steps": 0
} | 53 | 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, 14, 15, 17, 18, 19, 20, 22, 23, 24, 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_... |
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": 54, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 54,
"max_global_steps": 0,
"min_global_steps": 0
} | 54 | 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, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 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": 55, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 55,
"max_global_steps": 0,
"min_global_steps": 0
} | 55 | 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 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_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": 56, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 56,
"max_global_steps": 0,
"min_global_steps": 0
} | 56 | 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, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 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_... |
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": 57, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 57,
"max_global_steps": 0,
"min_global_steps": 0
} | 57 | 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, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 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": 58, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 58,
"max_global_steps": 0,
"min_global_steps": 0
} | 58 | 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, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 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_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": 59, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 59,
"max_global_steps": 0,
"min_global_steps": 0
} | 59 | 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, 31, 32, 33, 34, 36, 37, 38], "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": 60, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 60,
"max_global_steps": 0,
"min_global_steps": 0
} | 60 | 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, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], "metadata": {"evidence_size": 4, "family_bucket": "mixed", "maximum_separation": 0.5555555555555556, "mean_separation": 0.3703703703703704, "minimum_separa... |
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": 61, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 61,
"max_global_steps": 0,
"min_global_steps": 0
} | 61 | 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, 7, 8, 9, 11, 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": 4, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normaliz... |
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": 62, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 62,
"max_global_steps": 0,
"min_global_steps": 0
} | 62 | 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, 11, 12, 13, 14, 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": 63, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 63,
"max_global_steps": 0,
"min_global_steps": 0
} | 63 | 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, 6, 7, 8, 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, 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": 64, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 64,
"max_global_steps": 0,
"min_global_steps": 0
} | 64 | 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, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38], "metadata": {"evidence_size": 7, "family_bucket": "mixed", "maximum_separation": 0.6060606060606061, "mean_separation": 0.48484848484848486, "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": 65, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 65,
"max_global_steps": 0,
"min_global_steps": 0
} | 65 | 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 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_... |
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": 66, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 66,
"max_global_steps": 0,
"min_global_steps": 0
} | 66 | 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, 12, 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": 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": 67, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 67,
"max_global_steps": 0,
"min_global_steps": 0
} | 67 | 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, 29, 30, 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": 68, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 68,
"max_global_steps": 0,
"min_global_steps": 0
} | 68 | 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, 7, 8, 9, 10, 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": "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": 69, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 69,
"max_global_steps": 0,
"min_global_steps": 0
} | 69 | 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, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 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_... |
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": 70, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 70,
"max_global_steps": 0,
"min_global_steps": 0
} | 70 | 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, 16, 17, 18, 21, 22, 23, 24, 25, 26, 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": 71, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 71,
"max_global_steps": 0,
"min_global_steps": 0
} | 71 | 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": [5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 8, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mean_sepa... |
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": 72, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 72,
"max_global_steps": 0,
"min_global_steps": 0
} | 72 | 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": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 8, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mean_sepa... |
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": 73, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 73,
"max_global_steps": 0,
"min_global_steps": 0
} | 73 | 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, 12, 13, 14, 15, 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": 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": 74, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 74,
"max_global_steps": 0,
"min_global_steps": 0
} | 74 | 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, 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, 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": 75, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 75,
"max_global_steps": 0,
"min_global_steps": 0
} | 75 | 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, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "within_family", "maximum_separation": 0.5714285714285714, "mean_separation": 0.41904761904761906, "minimum... |
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": 76, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 76,
"max_global_steps": 0,
"min_global_steps": 0
} | 76 | 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, 11, 12, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 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_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": 77, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 77,
"max_global_steps": 0,
"min_global_steps": 0
} | 77 | 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": [5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 8, "family_bucket": "within_family", "maximum_separation": 0.0, "mean_separation": 0.0, "minimum_separation": 0.0, "normalized_mean_sepa... |
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": 78, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 78,
"max_global_steps": 0,
"min_global_steps": 0
} | 78 | 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, 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": 6, "family_bucket": "cross_family", "maximum_separation": 0.4117647058823529, "mean_separation": 0.29411764705882354, "minimum_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": 79, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 79,
"max_global_steps": 0,
"min_global_steps": 0
} | 79 | 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, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 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": 80, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 80,
"max_global_steps": 0,
"min_global_steps": 0
} | 80 | 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, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 23, 24, 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_... |
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": 81, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 81,
"max_global_steps": 0,
"min_global_steps": 0
} | 81 | 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, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 4, "family_bucket": "mixed", "maximum_separation": 0.5555555555555556, "mean_separation": 0.3148148148148148, "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": 82, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 82,
"max_global_steps": 0,
"min_global_steps": 0
} | 82 | 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, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 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": 83, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 83,
"max_global_steps": 0,
"min_global_steps": 0
} | 83 | 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, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 36, 38, 39], "metadata": {"evidence_size": 7, "family_bucket": "mixed", "maximum_separation": 0.6060606060606061, "mean_separation": 0.48484848484848486, "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": 84, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 84,
"max_global_steps": 0,
"min_global_steps": 0
} | 84 | 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, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 36, 37, 38, 39], "metadata": {"evidence_size": 7, "family_bucket": "mixed", "maximum_separation": 0.6060606060606061, "mean_separation": 0.48484848484848486, "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": 85, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 85,
"max_global_steps": 0,
"min_global_steps": 0
} | 85 | 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, 21, 22, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 6, "family_bucket": "cross_family", "maximum_separation": 0.4117647058823529, "mean_separation": 0.29411764705882354, "minimum_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": 86, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 86,
"max_global_steps": 0,
"min_global_steps": 0
} | 86 | 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, 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, 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, "normaliz... |
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": 87, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 87,
"max_global_steps": 0,
"min_global_steps": 0
} | 87 | 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, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "mixed", "maximum_separation": 0.5714285714285714, "mean_separation": 0.45714285714285713, "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": 88, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 88,
"max_global_steps": 0,
"min_global_steps": 0
} | 88 | 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, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], "metadata": {"evidence_size": 5, "family_bucket": "mixed", "maximum_separation": 0.5714285714285714, "mean_separation": 0.39999999999999997, "minimum_separati... |
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": 89, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 89,
"max_global_steps": 0,
"min_global_steps": 0
} | 89 | 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, 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, 36, 37, 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": 90, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 90,
"max_global_steps": 0,
"min_global_steps": 0
} | 90 | 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, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 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": 91, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 91,
"max_global_steps": 0,
"min_global_steps": 0
} | 91 | 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, 11, 13, 14, 15, 16, 17, 18, 19, 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_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": 92, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 92,
"max_global_steps": 0,
"min_global_steps": 0
} | 92 | 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 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_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": 93, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 93,
"max_global_steps": 0,
"min_global_steps": 0
} | 93 | 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, 8, 9, 10, 11, 12, 13, 14, 15, 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_sepa... |
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": 94, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 94,
"max_global_steps": 0,
"min_global_steps": 0
} | 94 | 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, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 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": 95, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 95,
"max_global_steps": 0,
"min_global_steps": 0
} | 95 | 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, 16, 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": 96, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 96,
"max_global_steps": 0,
"min_global_steps": 0
} | 96 | 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, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 22, 23, 24, 25, 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_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": 97, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 97,
"max_global_steps": 0,
"min_global_steps": 0
} | 97 | 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 39], "metadata": {"evidence_size": 6, "family_bucket": "within_family", "maximum_separation": 0.35294117647058826, "mean_separation": 0.2647058823529412, "minimum_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": 98, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 98,
"max_global_steps": 0,
"min_global_steps": 0
} | 98 | 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, 11, 12, 13, 14, 16, 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": 99, "task": {"evidence_consistent_reward": 0.0, "nonempty_output_reward": 0.0, "parse_valid_reward"... | {
"index": 99,
"max_global_steps": 0,
"min_global_steps": 0
} | 99 | 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, 18, 19, 21, 22, 23, 25, 26, 27, 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... |
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