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feat(env): Leg Counting Curriculum (#275)

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* leg  counting curriculum

---------

Co-authored-by: Andreas Koepf <andreas.koepf@provisio.com>
This commit is contained in:
Zafir Stojanovski 2025-03-07 19:15:18 +01:00 • committed by GitHub
parent b915565c0d
commit e560cb3c46
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3 changed files with 44 additions and 8 deletions
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3
reasoning_gym/arithmetic/__init__.py
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@ -14,7 +14,7 @@ from .fraction_simplification import FractionSimplificationConfig, FractionSimpl
from .gcd import GCDConfig, GCDDataset
from .gsm_symbolic.gsm_symbolic import GSMSymbolicDataset, GSMSymbolicDatasetConfig
from .lcm import LCMConfig, LCMDataset
from .leg_counting import LegCountingConfig, LegCountingDataset
from .leg_counting import LegCountingConfig, LegCountingCurriculum, LegCountingDataset
from .number_format import NumberFormatConfig, NumberFormatDataset
from .power_function import PowerFunctionConfig, PowerFunctionDataset
from .prime_factorization import PrimeFactorizationConfig, PrimeFactorizationDataset
@ -36,6 +36,7 @@ __all__ = [
"LCMDataset",
"LegCountingConfig",
"LegCountingDataset",
"LegCountingCurriculum",
"PowerFunctionConfig",
"PowerFunctionDataset",
"PrimeFactorizationConfig",

22
reasoning_gym/arithmetic/leg_counting.py
View file

@ -4,9 +4,7 @@ from dataclasses import dataclass
from random import Random
from typing import Optional
from reasoning_gym.coaching.attributes import AttributeType, RangeAttributeDefinition
from reasoning_gym.coaching.base_curriculum import BaseCurriculum
from ..coaching import AttributeType, BaseCurriculum, RangeAttributeDefinition
from ..factory import ProceduralDataset, register_dataset
ANIMALS = {
@ -69,6 +67,7 @@ class LegCountingConfig:
min_animals: int = 3 # Minimum number of animals in problem
max_animals: int = 10 # Maximum number of animals
min_instances: int = 1 # Minimum instances of each animal
max_instances: int = 15 # Maximum instances of each animal
seed: Optional[int] = None
size: int = 500 # Virtual dataset size
@ -77,7 +76,8 @@ class LegCountingConfig:
"""Validate configuration parameters"""
assert self.min_animals > 0, "min_animals must be positive"
assert self.max_animals >= self.min_animals, "max_animals must be >= min_animals"
assert self.max_instances > 0, "max_instances must be positive"
assert self.min_instances > 0, "min_instances must be positive"
assert self.max_instances >= self.min_instances, "max_instances must be >= min_instances"
class LegCountingDataset(ProceduralDataset):
@ -94,7 +94,7 @@ class LegCountingDataset(ProceduralDataset):
# Select random animals
selected_animals = rng.sample(list(ANIMALS.keys()), num_types)
for animal in selected_animals:
count = rng.randint(1, self.config.max_instances)
count = rng.randint(self.config.min_instances, self.config.max_instances)
animals[animal] = count
return animals
@ -136,13 +136,23 @@ class LegCountingCurriculum(BaseCurriculum):
RangeAttributeDefinition(
name="num_animals",
levels=list(range(1, 20)),
default_level=0, # Start with 2 terms
default_level=0,
description="Number of animals in question",
attr_type=AttributeType.APPEND,
min_value=1, # Ensure at least 1 animal
lower_field_name="min_animals",
upper_field_name="max_animals",
),
RangeAttributeDefinition(
name="num_instances",
levels=[2, 4, 8, 16, 32, 64, 128, 256, 512, 1024],
default_level=0,
description="Number of instances of each animal",
attr_type=AttributeType.APPEND,
min_value=1,
lower_field_name="min_instances",
upper_field_name="max_instances",
),
)