Datasets:

wenbochen111
/

MARS

---
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}
}
```

	---
	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}
	}
	```