BABYAI Environment: Validation Layer Analysis
Overview
BABYAI is one of the 8 validation environments. This document shows which validation scoring layers include BABYAI and how it contributes to the overall scoring system.
BABYAI Environment Details
- Environment Name:
agentgym:babyai - Type: AgentGym environment
- Dataset Size: 500 tasks
- Daily Sampling Rate: 120/day (fast environment)
- Task Type: Grid-world navigation and instruction following
- Max Rounds: 10
- Timeout: 1200 seconds
Validation Layers That Include BABYAI
BABYAI appears in validation layers 3-8 as part of various environment combinations. Here's the breakdown:
Layer 3 (3-environment combinations)
Total subsets with BABYAI: C(7,2) = 21 combinations
BABYAI appears in 21 out of 56 total Layer 3 subsets. Examples:
- {BABYAI, WEBSHOP, ALFWORLD}
- {BABYAI, SCIWORLD, TEXTCRAFT}
- {BABYAI, SAT, DED}
- {BABYAI, ABD, WEBSHOP}
- ... (17 more combinations)
Weight per subset: 8.0 / 56 = 0.143 Total potential weight from BABYAI Layer 3 subsets: 21 × 0.143 = 3.003
Layer 4 (4-environment combinations)
Total subsets with BABYAI: C(7,3) = 35 combinations
BABYAI appears in 35 out of 70 total Layer 4 subsets. Examples:
- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD}
- {BABYAI, TEXTCRAFT, SAT, DED}
- {BABYAI, ABD, WEBSHOP, ALFWORLD}
- ... (32 more combinations)
Weight per subset: 16.0 / 70 = 0.229 Total potential weight from BABYAI Layer 4 subsets: 35 × 0.229 = 8.015
Layer 5 (5-environment combinations)
Total subsets with BABYAI: C(7,4) = 35 combinations
BABYAI appears in 35 out of 56 total Layer 5 subsets. Examples:
- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT}
- {BABYAI, SAT, DED, ABD, WEBSHOP}
- ... (33 more combinations)
Weight per subset: 32.0 / 56 = 0.571 Total potential weight from BABYAI Layer 5 subsets: 35 × 0.571 = 19.985
Layer 6 (6-environment combinations)
Total subsets with BABYAI: C(7,5) = 21 combinations
BABYAI appears in 21 out of 28 total Layer 6 subsets. Examples:
- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT, SAT}
- {BABYAI, DED, ABD, WEBSHOP, ALFWORLD, SCIWORLD}
- ... (19 more combinations)
Weight per subset: 64.0 / 28 = 2.286 Total potential weight from BABYAI Layer 6 subsets: 21 × 2.286 = 48.006
Layer 7 (7-environment combinations)
Total subsets with BABYAI: C(7,6) = 7 combinations
BABYAI appears in 7 out of 8 total Layer 7 subsets. Examples:
- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT, SAT, DED}
- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT, SAT, ABD}
- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT, DED, ABD}
- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, SAT, DED, ABD}
- {BABYAI, WEBSHOP, ALFWORLD, TEXTCRAFT, SAT, DED, ABD}
- {BABYAI, WEBSHOP, SCIWORLD, TEXTCRAFT, SAT, DED, ABD}
- {BABYAI, ALFWORLD, SCIWORLD, TEXTCRAFT, SAT, DED, ABD}
Weight per subset: 128.0 / 8 = 16.0 Total potential weight from BABYAI Layer 7 subsets: 7 × 16.0 = 112.0
Layer 8 (All 8 environments)
Total subsets with BABYAI: C(7,7) = 1 combination
BABYAI appears in the single Layer 8 subset:
- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT, SAT, DED, ABD}
Weight per subset: 256.0 / 1 = 256.0 Total potential weight from BABYAI Layer 8 subset: 1 × 256.0 = 256.0
Summary Table
| Layer | Total Subsets | Subsets with BABYAI | Weight per Subset | Total BABYAI Weight |
|---|---|---|---|---|
| 3 | 56 | 21 | 0.143 | 3.003 |
| 4 | 70 | 35 | 0.229 | 8.015 |
| 5 | 56 | 35 | 0.571 | 19.985 |
| 6 | 28 | 21 | 2.286 | 48.006 |
| 7 | 8 | 7 | 16.0 | 112.0 |
| 8 | 1 | 1 | 256.0 | 256.0 |
| Total | 219 | 120 | - | 447.009 |
Key Insights
BABYAI Coverage: BABYAI appears in 120 out of 219 total evaluated subsets (54.8% of all subsets)
Exponential Importance: As layers increase, BABYAI's potential contribution grows exponentially:
- Layer 3: 3.003 total weight
- Layer 8: 256.0 total weight (85× more!)
Comprehensive Performance Matters: To maximize BABYAI-related rewards, a model must:
- Perform well on BABYAI alone (but this isn't evaluated in layers 1-2)
- Perform well on BABYAI + 2 other environments (Layer 3)
- Perform well on BABYAI + 3 other environments (Layer 4)
- ...
- Perform well on ALL 8 environments including BABYAI (Layer 8) - highest reward!
Layer 8 Dominance: The single Layer 8 subset (all environments) contributes 256.0 weight, which is more than all other BABYAI-related subsets combined (191.009).
Relationship to 36 Transformer Layers
The 36 transformer layers in the model architecture are not directly mapped to BABYAI. Instead:
- All 36 layers work together to process BABYAI tasks
- BABYAI performance is evaluated across all 8 environments
- Validation scoring layers (3-8) reward models that perform well on BABYAI in combination with other environments
However, based on the codebase documentation (BABYAI_SPECIFIC_IMPROVEMENTS.md), there's evidence that:
- Late layers (24-35) may be more important for BABYAI-specific improvements
- BABYAI tasks (navigation/instruction-following) may benefit more from higher-level reasoning in later transformer layers
Scoring Example
If a model performs well on BABYAI:
Scenario A: Model excels on BABYAI + 2 other environments (wins 5 Layer 3 subsets)
- Reward: 5 × 0.143 = 0.715
Scenario B: Model excels on BABYAI + 6 other environments (wins 1 Layer 7 subset)
- Reward: 1 × 16.0 = 16.0
Scenario C: Model excels on ALL 8 environments including BABYAI (wins Layer 8)
- Reward: 1 × 256.0 = 256.0
Result: Comprehensive performance (Scenario C) gives 358× more reward than partial performance (Scenario A)!
Conclusion
BABYAI is a critical component of the validation system, appearing in:
- 120 out of 219 evaluated subsets (54.8%)
- All validation layers 3-8
- Maximum reward potential of 447.009 (if model wins all BABYAI-related subsets)
To maximize BABYAI-related rewards, models must demonstrate comprehensive ability across multiple environments, with the highest reward (256.0) coming from performing well on all 8 environments simultaneously.