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Datasets:
mondk
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polyglot-hardbug-rl

RL Environment
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
verifiers
code
bug-fixing
swe-bench
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metadata 
license: mit
language:
  - en
task_categories:
  - text-generation
tags:
  - rl-environment
  - verifiers
  - code
  - bug-fixing
  - swe-bench
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet

Polyglot HardBug RL

A Hugging Face RL environment dataset of 10 expert-level, multi-language bug-fixing tasks for training and evaluating coding agents. Each task is a self-contained SWE-bench-style instance: a real buggy project, a precise problem statement, hidden verification tests, and a gold patch.

Every task was authored to reproduce a class of subtle defect that standard datasets miss - the bugs are semantically rich (concurrency, three-valued SQL logic, numeric type systems, Unicode canonicalization) rather than syntactic slips, and each one was verified bidirectionally: the buggy repository fails exactly the declared fail_to_pass tests, and the gold patch makes the whole suite pass.

Tasks by language

Language Tasks
javascript 1
python 7
sql 1
typescript 1

Row schema

Column Description
task_id stable task identifier (also the HF row id)
title one-line description of the defect
language python / javascript / typescript / sql
category defect class, e.g. concurrency-async, sql-semantics
difficulty difficulty rating (all rows: expert)
source provenance (original-synthetic: authored for this dataset)
license per-row license (MIT)
created authoring date
prompt the agent-facing problem statement (markdown)
repo_files the buggy workspace: list of {filename, content} entries
test_files the hidden verification suite (same entry format)
gold_patch unified diff (a//b/ prefixes) fixing the bug
test_command argv that runs the hidden suite inside the sandbox
fail_to_pass tests that fail on the buggy repo and pass after the fix
pass_to_pass tests that must keep passing (regression guard)
time_limit_s test-run budget in seconds
sandbox_image runtime image for the task (python or node toolchain)
sandbox_system_deps extra system packages the image needs (e.g. tzdata)
  • repo_files entries are sorted by filename; rebuild a dict with {e["filename"]: e["content"] for e in repo_files}. The agent edits this state (typically by emitting a unified diff).
  • test_files holds the hidden verification suite, injected by the harness after the agent finishes, so any test edits the agent made are overwritten.
  • gold_patch is patch -p1 compatible and transforms repo_files into the fixed state.

Verification protocol

The suite is TAP-based. Each test prints ok N - name or not ok N - name with a diagnostic; the process exits non-zero if any test failed. A solution is resolved iff every fail_to_pass test passes and every pass_to_pass test still passes.

Intended use

Load with the companion verifiers environment package (polyglot-hardbug-rl/environment/), or drive the rows with your own harness:

from datasets import load_dataset

rows = load_dataset("mondk/polyglot-hardbug-rl", split="train")
for row in rows:
    write_workspace(row["repo_files"])          # agent edits this
    apply_patch(agent_patch)                    # model's diff
    inject(row["test_files"])                   # hidden tests
    stdout = run(row["test_command"], timeout=row["time_limit_s"])
    score = score_tap(stdout, row["fail_to_pass"], row["pass_to_pass"])

Repository layout

path contents
data/ the published dataset (train.jsonl, train-00000-of-00001.parquet)
tasks/ one directory per task: problem.md, repo/ (buggy), fixed/ (gold), tests/ (hidden TAP suite), meta.json, run.json
scripts/build_dataset.py rebuilds data/ and this card from tasks/; enforces the schema and the pure-ASCII English gate
scripts/validate_dataset.py end-to-end check: buggy fails exactly fail_to_pass, gold patch makes the whole suite pass; writes validation_report.md
environment/ the verifiers v1 environment package (polyglot_hardbug) plus a standalone local runner
validation_report.md result of the latest validate_dataset.py run

License

MIT (see LICENSE). Task content is original synthetic work created for this dataset; no third-party source code is included.