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61 lines
2.3 KiB
Markdown
61 lines
2.3 KiB
Markdown
# Regex Generation Environment
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An RL environment that trains language models to generate correct Python-compatible regular expressions from natural language descriptions and example test cases.
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## How it works
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Each problem gives the model:
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- A natural language description of the pattern to match
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- A set of strings that **should** match
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- A set of strings that **should not** match
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The model must produce a regex pattern inside `<answer>` tags. The pattern is tested using `re.fullmatch()` against all provided examples.
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## Reward signal
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The reward is the fraction of test cases passed (both positive and negative). A score of 1.0 means the regex correctly matches all positive examples and rejects all negative ones. Groups where all rollouts score identically are discarded (no learning signal).
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## Problem set
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The environment ships with 28 hand-crafted regex problems across three difficulty levels:
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- **Easy**: Basic patterns (digits only, starts with X, exact match)
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- **Medium**: Emails, dates, phone numbers, hex colors, zip codes
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- **Hard**: IPv4 addresses, semantic versioning, URLs, repeated words
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Problems are split 80/20 into train/test sets.
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## Running
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```bash
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# Basic training
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python regex_env.py serve \
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--env.tokenizer_name="NousResearch/DeepHermes-3-Llama-3-3B-Preview" \
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--openai.base_url http://localhost:9001/v1
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# Only easy/medium problems
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python regex_env.py serve \
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--env.difficulties='["easy", "medium"]'
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```
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## Config options
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| Option | Type | Default | Description |
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|--------|------|---------|-------------|
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| `difficulties` | list[str] | `["easy", "medium", "hard"]` | Difficulty levels to include |
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| `score_threshold` | float | `1.0` | Min score to count as "correct" in metrics |
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Standard `BaseEnvConfig` options (`group_size`, `max_token_length`, etc.) also apply.
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## Eval metrics
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| Metric | Description |
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|--------|-------------|
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| `eval/avg_score` | Average fraction of test cases passed |
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| `eval/percent_perfect` | Fraction of problems with all tests passing |
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| `eval/percent_valid_regex` | Fraction of responses with syntactically valid regex |
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| `train/percent_correct` | Training accuracy (problems scoring above threshold) |
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## Dependencies
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No extra dependencies beyond what Atropos already provides. Uses only Python's built-in `re` module for regex validation.
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