mirror of
https://github.com/open-thought/reasoning-gym.git
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4b60c32978
* began curr exp * added holdout words * updated config * added context * updated base curriculum * updaed * updated curriculum * updated * updated * updated automatic flag * updated ray trainer * update
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
"""Spell backward task generator"""
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import re
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from dataclasses import dataclass
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from random import Random
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from typing import Any, Optional
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from ..coaching import BaseCurriculum, RangeAttributeDefinition
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from ..data import read_data_file
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from ..factory import ProceduralDataset, register_dataset
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DATASET_NAME = "spell_backward"
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@dataclass
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class SpellBackwardConfig:
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"""Configuration for spelling words backward task generation"""
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min_word_len: int = 3 # Minimum word length
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max_word_len: int = 10 # Maximum word length
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seed: Optional[int] = None
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data_file: str = "words3to10.txt"
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size: int = 500 # Virtual dataset size
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def validate(self) -> None:
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"""Validate configuration parameters"""
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assert self.min_word_len > 0, "min_word_len must be positive"
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assert self.max_word_len >= self.min_word_len, "max_word_len must be >= min_word_len"
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class SpellBackwardDataset(ProceduralDataset):
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"""Generates tasks to spell words backward"""
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def __init__(self, config: SpellBackwardConfig):
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super().__init__(config=config, seed=config.seed, size=config.size)
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# Load and preprocess text
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text = read_data_file(self.config.data_file)
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self.words = [
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word.strip()
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for word in text.splitlines()
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if word.strip().isalnum() and config.min_word_len <= len(word.strip()) <= config.max_word_len
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]
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def __getitem__(self, idx: int) -> dict:
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"""Generate a single spell backward task"""
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rng = Random(self.seed + idx)
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# Select random word
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word = rng.choice(self.words)
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answer = word[::-1]
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return {
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"question": f"Spell this word backward (example: sun -> nus): {word}",
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"answer": answer,
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"metadata": {
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"source_dataset": DATASET_NAME,
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"source_index": idx,
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"word": word,
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"word_len": len(word),
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"difficulty": {
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"word_len": (self.config.min_word_len, self.config.max_word_len),
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},
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},
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}
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def score_answer(self, answer: Optional[str], entry: dict[str, Any]) -> float:
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reward = 0.0
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expected_answer = entry["answer"]
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if isinstance(answer, str):
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try:
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expected_answer = expected_answer.lower()
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answer = answer.lower()
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if expected_answer == answer:
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return 1.0
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else:
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answer_len = len(expected_answer)
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for i in range(len(expected_answer)):
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if i < len(expected_answer) and i < len(answer):
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if expected_answer[i] == answer[i]:
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reward += 1 / answer_len
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else:
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continue
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else:
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break
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if reward == 1.0:
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reward -= 0.2
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except:
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reward = 0.0
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return reward
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class SpellBackwardCurriculum(BaseCurriculum):
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def __init__(self):
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super().__init__(SpellBackwardCurriculum.__name__, SpellBackwardConfig)
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# Define attributes
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self._define_attributes(
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RangeAttributeDefinition(
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name="word_len",
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levels=list(range(3, 13, 2)),
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description="Word length",
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lower_field_name="min_word_len",
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upper_field_name="max_word_len",
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ensure_interval=False,
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),
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)
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register_dataset(DATASET_NAME, SpellBackwardDataset, SpellBackwardConfig, SpellBackwardCurriculum)
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