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re-polar

Tasks:
Text Generation
Modalities:
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Languages:
English
Size:
10M - 100M
ArXiv:
Tags:
llm
layer-skipping
layer-repetition
programs-of-layers
mcts
routing
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metadata 
pretty_name: RE-PoLar
license: cc-by-nc-4.0
language:
  - en
tags:
  - llm
  - layer-skipping
  - layer-repetition
  - programs-of-layers
  - mcts
  - routing
  - reproduction
  - math-reasoning
task_categories:
  - text-generation
size_categories:
  - 10M<n<100M
configs:
  - config_name: programs_qwen15_moe_a27b
    field: samples
    data_files:
      - split: seed42
        path: mcts_results/qwen15_moe_a27b/seed42/*/merged_mcts_samples.json.gz
      - split: seed1
        path: mcts_results/qwen15_moe_a27b/seed1/*/merged_mcts_samples.json.gz
      - split: seed7
        path: mcts_results/qwen15_moe_a27b/seed7/*/merged_mcts_samples.json.gz
  - config_name: programs_qwen25_3b
    field: samples
    data_files:
      - split: seed42
        path: mcts_results/qwen25_3b/seed42/*/merged_mcts_samples.json.gz
      - split: seed1
        path: mcts_results/qwen25_3b/seed1/*/merged_mcts_samples.json.gz
      - split: seed7
        path: mcts_results/qwen25_3b/seed7/*/merged_mcts_samples.json.gz
  - config_name: programs_qwen25_7b
    field: samples
    data_files:
      - split: seed42
        path: mcts_results/qwen25_7b/seed42/*/merged_mcts_samples.json.gz
      - split: seed1
        path: mcts_results/qwen25_7b/seed1/*/merged_mcts_samples.json.gz
      - split: seed7
        path: mcts_results/qwen25_7b/seed7/*/merged_mcts_samples.json.gz
  - config_name: programs_qwen3_8b
    field: samples
    data_files:
      - split: seed42
        path: mcts_results/qwen3_8b/seed42/*/merged_mcts_samples.json.gz
      - split: seed1
        path: mcts_results/qwen3_8b/seed1/*/merged_mcts_samples.json.gz
      - split: seed7
        path: mcts_results/qwen3_8b/seed7/*/merged_mcts_samples.json.gz
  - config_name: programs_qwen3_32b
    field: samples
    data_files:
      - split: seed42
        path: mcts_results/qwen3_32b/seed42/*/merged_mcts_samples.json.gz
      - split: seed1
        path: mcts_results/qwen3_32b/seed1/*/merged_mcts_samples.json.gz
      - split: seed7
        path: mcts_results/qwen3_32b/seed7/*/merged_mcts_samples.json.gz
  - config_name: programs_llama32_3b
    field: samples
    data_files:
      - split: seed42
        path: mcts_results/llama32_3b/seed42/*/merged_mcts_samples.json.gz
      - split: seed1
        path: mcts_results/llama32_3b/seed1/*/merged_mcts_samples.json.gz
      - split: seed7
        path: mcts_results/llama32_3b/seed7/*/merged_mcts_samples.json.gz
  - config_name: answers_qwen15_moe_a27b
    data_files:
      - split: seed42
        path: generated_answers/qwen15_moe_a27b/seed42/*.jsonl.gz
      - split: seed1
        path: generated_answers/qwen15_moe_a27b/seed1/*.jsonl.gz
      - split: seed7
        path: generated_answers/qwen15_moe_a27b/seed7/*.jsonl.gz
  - config_name: answers_qwen25_3b
    data_files:
      - split: seed42
        path: generated_answers/qwen25_3b/seed42/*.jsonl.gz
      - split: seed1
        path: generated_answers/qwen25_3b/seed1/*.jsonl.gz
      - split: seed7
        path: generated_answers/qwen25_3b/seed7/*.jsonl.gz
  - config_name: answers_qwen25_7b
    data_files:
      - split: seed42
        path: generated_answers/qwen25_7b/seed42/*.jsonl.gz
      - split: seed1
        path: generated_answers/qwen25_7b/seed1/*.jsonl.gz
      - split: seed7
        path: generated_answers/qwen25_7b/seed7/*.jsonl.gz
  - config_name: answers_qwen3_8b
    data_files:
      - split: seed42
        path: generated_answers/qwen3_8b/seed42/*.jsonl.gz
      - split: seed1
        path: generated_answers/qwen3_8b/seed1/*.jsonl.gz
      - split: seed7
        path: generated_answers/qwen3_8b/seed7/*.jsonl.gz
  - config_name: answers_qwen3_32b
    data_files:
      - split: seed42
        path: generated_answers/qwen3_32b/seed42/*.jsonl.gz
      - split: seed1
        path: generated_answers/qwen3_32b/seed1/*.jsonl.gz
      - split: seed7
        path: generated_answers/qwen3_32b/seed7/*.jsonl.gz
  - config_name: answers_llama32_3b
    data_files:
      - split: seed42
        path: generated_answers/llama32_3b/seed42/*.jsonl.gz
      - split: seed1
        path: generated_answers/llama32_3b/seed1/*.jsonl.gz
      - split: seed7
        path: generated_answers/llama32_3b/seed7/*.jsonl.gz
  - config_name: polar_split_programs_qwen15_moe_a27b
    field: samples
    data_files:
      - split: polar_default_seed42
        path: >-
          polar_split/mcts_results/qwen15_moe_a27b/polar_default/seed42/*/merged_mcts_samples.json.gz
      - split: polar_default_seed1
        path: >-
          polar_split/mcts_results/qwen15_moe_a27b/polar_default/seed1/*/merged_mcts_samples.json.gz
      - split: polar_default_seed7
        path: >-
          polar_split/mcts_results/qwen15_moe_a27b/polar_default/seed7/*/merged_mcts_samples.json.gz
      - split: oneshot_seed42
        path: >-
          polar_split/mcts_results/qwen15_moe_a27b/oneshot/seed42/*/merged_mcts_samples.json.gz
      - split: oneshot_seed1
        path: >-
          polar_split/mcts_results/qwen15_moe_a27b/oneshot/seed1/*/merged_mcts_samples.json.gz
      - split: oneshot_seed7
        path: >-
          polar_split/mcts_results/qwen15_moe_a27b/oneshot/seed7/*/merged_mcts_samples.json.gz
  - config_name: polar_split_programs_qwen25_3b
    field: samples
    data_files:
      - split: polar_default_seed42
        path: >-
          polar_split/mcts_results/qwen25_3b/polar_default/seed42/*/merged_mcts_samples.json.gz
      - split: polar_default_seed1
        path: >-
          polar_split/mcts_results/qwen25_3b/polar_default/seed1/*/merged_mcts_samples.json.gz
      - split: polar_default_seed7
        path: >-
          polar_split/mcts_results/qwen25_3b/polar_default/seed7/*/merged_mcts_samples.json.gz
      - split: oneshot_seed42
        path: >-
          polar_split/mcts_results/qwen25_3b/oneshot/seed42/*/merged_mcts_samples.json.gz
      - split: oneshot_seed1
        path: >-
          polar_split/mcts_results/qwen25_3b/oneshot/seed1/*/merged_mcts_samples.json.gz
      - split: oneshot_seed7
        path: >-
          polar_split/mcts_results/qwen25_3b/oneshot/seed7/*/merged_mcts_samples.json.gz
  - config_name: polar_split_programs_qwen3_8b
    field: samples
    data_files:
      - split: polar_default_seed42
        path: >-
          polar_split/mcts_results/qwen3_8b/polar_default/seed42/*/merged_mcts_samples.json.gz
      - split: polar_default_seed1
        path: >-
          polar_split/mcts_results/qwen3_8b/polar_default/seed1/*/merged_mcts_samples.json.gz
      - split: polar_default_seed7
        path: >-
          polar_split/mcts_results/qwen3_8b/polar_default/seed7/*/merged_mcts_samples.json.gz
      - split: oneshot_seed42
        path: >-
          polar_split/mcts_results/qwen3_8b/oneshot/seed42/*/merged_mcts_samples.json.gz
      - split: oneshot_seed1
        path: >-
          polar_split/mcts_results/qwen3_8b/oneshot/seed1/*/merged_mcts_samples.json.gz
      - split: oneshot_seed7
        path: >-
          polar_split/mcts_results/qwen3_8b/oneshot/seed7/*/merged_mcts_samples.json.gz
  - config_name: polar_split_programs_llama32_3b
    field: samples
    data_files:
      - split: polar_default_seed42
        path: >-
          polar_split/mcts_results/llama32_3b/polar_default/seed42/*/merged_mcts_samples.json.gz
      - split: polar_default_seed1
        path: >-
          polar_split/mcts_results/llama32_3b/polar_default/seed1/*/merged_mcts_samples.json.gz
      - split: polar_default_seed7
        path: >-
          polar_split/mcts_results/llama32_3b/polar_default/seed7/*/merged_mcts_samples.json.gz
      - split: oneshot_seed42
        path: >-
          polar_split/mcts_results/llama32_3b/oneshot/seed42/*/merged_mcts_samples.json.gz
      - split: oneshot_seed1
        path: >-
          polar_split/mcts_results/llama32_3b/oneshot/seed1/*/merged_mcts_samples.json.gz
      - split: oneshot_seed7
        path: >-
          polar_split/mcts_results/llama32_3b/oneshot/seed7/*/merged_mcts_samples.json.gz

RE-PoLar: every program-of-layers our searches evaluated

Overview

A program-of-layers runs a transformer's layers in a different order than the standard forward pass: some layers are skipped, some segments repeated. PoLar finds such programs per question with a Monte Carlo Tree Search (MCTS) and trains a router that predicts one from the question. This dataset holds everything our reproduction's searches, routers and fixed programs produced:

  • Main track (our DART-Math split, our search, oneshot prompt): 6 models × 3 search seeds × 5 difficulty levels × 2,000 questions. Every program each search evaluated, with its verdict (56.6M programs in the whole dataset, 4.6M of them solving their question), the model's generated answer behind each verdict (53.2M answers), the trained routers and their test results, the best fixed program per level, and the menu cross-execution.
  • PoLar split (polar_split/, paper Appendix B): Li et al.'s released search on their own MATH question sets, 4 models × 2 prompts (polar_default, oneshot) × 3 seeds, with 4-fold cross-validated routers and fixed programs, and routers evaluated out of distribution (ASDiv, MAWPS, MMLU-Pro).
  • Cross-checks (cross_checks/): their search on our split and our search on theirs.

Models: Qwen1.5-MoE-A2.7B-Chat, Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct, Qwen3-8B, Qwen3-32B, LLaMA-3.2-3B-Instruct.

A router reads the prompt and picks a per-layer skip/keep/repeat program before the frozen transformer runs.

Explore the search trees

The dataset viewer above shows the searches as tables: pick programs_<model> for the programs and verdicts of each question (split = search seed), answers_<model> for the generated answers, polar_split_programs_<model> for the PoLar split. The search trees themselves, node by node, are on the project page's interactive explorer.

Layout 

mcts_results/<model>/seed<S>/dart-math-diff-<L>/merged_mcts_samples.json.gz   programs + verdicts per question
generated_answers/<model>/seed<S>/diff<L>.jsonl.gz                            one generated answer per evaluation
router_checkpoints/<model>/seed<S>/diff<L>.pt                                 trained routers (weights only)
router_results/<model>/seed<S>/diff<L>.json                                   router test results, per question
fixed_program/<model>/seed<S>/diff<L>.json                                    fixed-program baseline, per question
menu_crossexec/<model>/diff<L>.json.gz                                        top-100 programs x 2,000 questions
menu_crossexec_heldout/<model>/diff<L>.json.gz                                top-100 by train+val, run on the 500 test questions
polar_split/...                                                               same artifacts under <model>/<prompt>/seed<S>/
cross_checks/{their_search_our_split,our_search_polar_split}/...
DATA.md                                                                       full schema of every file

<model>: qwen15_moe_a27b, qwen25_3b, qwen25_7b, qwen3_8b, qwen3_32b, llama32_3b; <S>: search seed 42, 1, 7; <L>: difficulty level 1-5. DATA.md documents every field, the search configurations and which runs have no answer texts.

Download and use

The code expects the files under data/ of a clone of the repository:

git clone https://github.com/DATEXIS/RE-PoLar && cd RE-PoLar
pip install -U huggingface_hub
hf download SWAMBIT/re-polar --repo-type dataset --local-dir data                 # everything, ~6 GB
hf download SWAMBIT/re-polar --repo-type dataset --local-dir data --include "router_results/*" "fixed_program/*"

One file with the datasets library, or directly:

from datasets import load_dataset
programs = load_dataset("SWAMBIT/re-polar", "programs_qwen3_8b", split="seed42")
print(programs[0]["sample_info"]["query_id"], len(programs[0]["final_valid_transitions"]))

import gzip, json
router = json.load(open("data/router_results/qwen3_8b/seed42/diff1.json"))
print(router["overall"])  # Base tau=0, router p@1 and p@5 on the 500 test questions
with gzip.open("data/generated_answers/qwen3_8b/seed42/diff1.jsonl.gz", "rt") as f:
    print(json.loads(next(f)))

Questions are not included

No file holds question text or reference answers, only query_ids. The MATH half of the DART-Math pool traces back to the delisted hendrycks/competition_math, so the questions are rebuilt locally from the public pool with the repository's code and joined on query_id (re_polar.datasets.dart_math, re_polar.datasets.polar_split, re_polar.datasets.attach_mcts_questions; see the repository README). The models sometimes restate their question in a generated answer; such restatements are replaced by [question] (DATA.md, "Generated text").

Citation 

@article{westerhoff2026repolar,
      title={Programs-of-Layers in LLMs through the Lens of Cortical Areas},
      author={Westerhoff, Justus and Olbrich, Stephan and Oraby, Hatem and Senn, Walter and Larkum, Matthew Evan and Gers, Felix Alexander},
      journal={arXiv preprint arXiv:2609.31360},
      year={2026}
}

License

CC BY-NC 4.0. The generated answers and verdicts are outputs of the models above and also fall under their licenses: Qwen1.5-MoE-A2.7B-Chat (Tongyi Qianwen License), Qwen2.5-3B-Instruct (Qwen Research License), Qwen2.5-7B-Instruct, Qwen3-8B and Qwen3-32B (Apache 2.0), LLaMA-3.2-3B-Instruct (Llama 3.2 Community License; Built with Llama). The routers embed questions with Qwen3-Embedding-0.6B (Apache 2.0). The code is MIT-licensed, see the repository.