---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
pretty_name: EffiBench (verl efficiency eval set)
license: other
language:
- en
task_categories:
- text-generation
tags:
- code
- code-generation
- code-efficiency
- benchmark
- evaluation
- verl
---

# EffiBench (verl efficiency eval set)

A canonical-validated subset of [DONG19/EffiBench](https://huggingface.co/datasets/DONG19/EffiBench)
(arXiv [2402.02037](https://arxiv.org/abs/2402.02037); 1000 LeetCode-style
Python tasks), converted to the verl rule-reward schema by
`verl/scripts/data/effibench.py`. **License: unspecified upstream** (no license
tag on the HF dataset or the GitHub repo) -- treat as research-only until
clarified.

**Row filter (854/1000 kept).** The upstream `test_case` column is broken for a
sizeable minority of rows: tree/linked-list tasks contain literal
`<__main__.TreeNode object at 0x...>` reprs inside asserts, in-place tasks
assert on never-defined variables, 14 rows have no tests at all. The filter is
executable, not heuristic: a row is kept iff its own canonical solution passes
its own assert suite through the actual grading harness (974 structurally
usable, 854 pass). The canonical's build-machine runtime/peak-RSS are stored in
`extra_info.reference_runtime_ms`/`reference_memory_mb` as fallback references.

**Scoring** (`verl/verl/utils/reward_score/effibench.py`): the extracted
`class Solution` runs the assert suite in ONE timed firejail execution; runtime
(`perf_counter` around the assert loop) and peak RSS (`getrusage(RUSAGE_SELF)`)
are measured in-process and compared against the canonical solution re-measured
on the same machine (cached per task).
`reward = 0.5*pass + 0.25*min(1, canon_rt/rt) + 0.25*min(1, canon_mem/mem)`.
Eval recipe: `verl/recipe/run_effibench_eval.sh`.

## Known compromises vs the official pipeline

- Memory is **peak RSS**, not the paper's memory_profiler time-integral
  (TMU/NMU); the ~20MB interpreter baseline sits on both sides of the ratio and
  biases it toward 1 on small tasks.
- Assert suites are binary (first failing assert aborts): no per-case fraction.
- Efficiency ratios are clipped at 1 -- beating the canonical earns full
  credit, not extra credit.
- Prompts use a `class Solution` starter skeleton reconstructed from the
  canonical solution (the source ships no starter-code column).