---
license: cc-by-4.0
task_categories:
  - text-generation
language:
  - en
tags:
  - reasoning
  - test-time-scaling
  - parallel-decoding
  - math
  - early-stopping
  - confidence
pretty_name: MARS Reasoning Trace Pools
size_categories:
  - 1M<n<10M
---

# MARS Reasoning Trace Pools

Pre-generated reasoning-trace pools for
**[MARS](https://github.com/Wenbo11/MARS)** - Margin-Adversarial Risk-controlled
Stopping, an early-stopping method for parallel LLM reasoning.

Paper: [MARS: Margin-Adversarial Risk-controlled Stopping for Parallel LLM Test-time Scaling](https://arxiv.org/abs/2606.12935) (arXiv:2606.12935).

Download these traces to **exactly reproduce the paper's benchmark numbers**
using the open-source MARS simulator.

## What is stored

Each trace record holds:
- **Per-token confidence trajectory** (`confs`: float16 array, ~2k–8k values)
- **Intermediate answers** probed every 2,048 tokens (`probes`: answer +
  is\_correct + confidence + raw boxed text at each checkpoint)
- **Final extracted answer** and **ground truth**

**These are confidence trajectories and intermediate-answer records, not
full reasoning text.** The raw chain-of-thought is not stored.
The text fields hold answer snippets only: `raw_text` is the boxed answer plus up to ~300 characters that follow it.
For a small number of traces (under 100 per file) where final-answer extraction failed, `extracted_answer` holds a longer span of the trace text.

## Contents

3 reasoning models × 3 competition-math benchmarks (30 problems each):

| File | Model | Benchmark | Traces |
|---|---|---|---|
| `DeepSeek-8B/aime25_deepseek.parquet` | DeepSeek-R1-0528-Qwen3-8B | AIME 2025 | 4,096/problem |
| `DeepSeek-8B/brumo25_deepseek.parquet` | DeepSeek-R1-0528-Qwen3-8B | BRUMO 2025 | 4,096/problem |
| `DeepSeek-8B/hmmt25_deepseek.parquet` | DeepSeek-R1-0528-Qwen3-8B | HMMT | 4,096/problem |
| `Qwen3-32B/aime25.parquet` | Qwen3-32B | AIME 2025 | 4,096/problem |
| `Qwen3-32B/brumo25.parquet` | Qwen3-32B | BRUMO 2025 | 4,096/problem |
| `Qwen3-32B/hmmt.parquet` | Qwen3-32B | HMMT | 4,096/problem |
| `Qwen3-next/aime25_thinking.parquet` | Qwen3-Next-80B-A3B-Thinking | AIME 2025 | 4,096/problem |
| `Qwen3-next/brumo25_thinking.parquet` | Qwen3-Next-80B-A3B-Thinking | BRUMO 2025 | 4,096/problem |
| `Qwen3-next/hmmt_thinking.parquet` | Qwen3-Next-80B-A3B-Thinking | HMMT | 4,096/problem |

~35 GB total (zstd-compressed parquet). ~1.1M traces across all files.

A few traces are missing: Qwen3-32B AIME 2025 has 122,878 rows, Qwen3-32B BRUMO 2025 has 122,876, and Qwen3-Next BRUMO 2025 and HMMT have 122,879 each (all other files have 30 x 4,096 = 122,880).

## Schema

One row per trace:

| Column | Type | Description |
|---|---|---|
| `qid` | int64 | Question ID (0-indexed within the benchmark) |
| `trace_idx` | int64 | Trace index within the question (0 to 4095) |
| `ground_truth` | string | Correct answer for the question |
| `extracted_answer` | string (nullable) | Final answer parsed from the full trace |
| `confs` | list\<float16\> | Per-token confidence score, full trace length |
| `probes` | list\<struct\> | Intermediate answers at checkpoints (see below) |

Each `probes` element: `{position: int64, answer: string, is_correct: bool,
raw_text: string, avg_conf: float64}`. Positions are checkpoint token indices
(multiples of 2048, plus the per-trace termination position).

Ground truth is embedded in every row, so the files are self-contained - no
separate answer key is needed.

## Reproduce the paper numbers

```bash
# 1. Get the simulator
git clone https://github.com/Wenbo11/MARS.git
cd MARS
uv sync

# 2. Download traces into ./data/  (auto-detected by the runner)
hf download wenbochen111/MARS --repo-type=dataset --local-dir ./data

# 3. Run calibrated MARS on any setting (same command for pkl or parquet)
uv run python examples/run_experiment.py \
    --model deepseek-8b --dataset aime-2025 \
    --method sc-mars-cal \
    --warmup-gamma --ucb-z 1.0 --gamma-min 0.5

# All reported runs use:
# --budget 512 --iterations 64 --warmup 16 --window 2048 --seed 42
```

The runner auto-detects the parquet files - no flag changes needed vs. the pkl
format. Results can be diffed against the reference CSVs in `repro_reference/`.

## Reference results

`repro_reference/{Model}/{dataset}/{method}/summary_overall.csv` and
`summary_per_question.csv` hold the exact per-question accuracy and savings
numbers from the paper's benchmark runs. Use these to verify your reproduction
matches to the decimal.

## Reproducibility notes

Exact reproduction requires:
- The Dynasor version pinned in the code repo's `uv.lock` (for answer
  equivalence scoring - `dynasor.core.evaluator.math_equal`). The answer
  grouping is frozen in the simulator cache, so downstream numbers are stable
  once the cache is built.
- NumPy ≥1.24 (for the bootstrap RNG, which is stable across patch versions).

## Provenance

Traces were generated using SGLang with the following models:
- **DeepSeek-R1-0528-Qwen3-8B** - [DeepSeek-AI](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B); MIT license
- **Qwen3-32B** - [Qwen](https://huggingface.co/Qwen/Qwen3-32B); Apache 2.0 license
- **Qwen3-Next-80B-A3B-Thinking** - [Qwen](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking); Apache 2.0 license

Problem sets: AIME 2025, BRUMO 2025, HMMT (competition math - problems are
public; no problem text is stored in this dataset).

This dataset is released under **CC-BY-4.0**.

## Citation

If you use this dataset, please cite the MARS paper:

```bibtex
@misc{chen2026mars,
  title         = {{MARS}: Margin-Adversarial Risk-controlled Stopping for Parallel {LLM} Test-time Scaling},
  author        = {Chen, Wenbo and Li, Puheng and Liu, Mengyang and Su, Weijie and Xie, Tianpei},
  year          = {2026},
  eprint        = {2606.12935},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2606.12935}
}
```