zml5418/thinking-leakage-rollouts

Thinking Leakage: rollouts

All model generations behind the paper Thinking Leakage: A Causal Audit of NoThink Post-Training in Hybrid Reasoning Models: 199,680 rollouts, 16 samples for each of 120 competition math problems, from Qwen3-8B, Qwen3-4B and MiniCPM4.1-8B before and after NoThink post-training (GRPO, OPSD, OPSD-LoRA, SFT on DAPO-Math), plus the activation-steering runs reported in the main text. The trained checkpoints are released as zml5418/{Method}-{Model} (for example zml5418/GRPO-Qwen3-8B, one branch per training step). Generation and evaluation details are given in the paper.

Configs

config	contents	files
base	base models in Think and NoThink mode	6
GRPO	every saved GRPO checkpoint (steps 50 to 500) in NoThink mode, plus GRPO-Qwen3-8B steps 200 and 500 in Think mode	32
OPSD	OPSD checkpoints, steps 25 to 100, NoThink	12
OPSD-LoRA	OPSD-LoRA checkpoints, steps 25 to 100, NoThink	12
SFT	SFT checkpoints, steps 25 to 100, NoThink	12
steer	base model in NoThink mode with alpha * v_leak added at layer 20, five doses per model	15
counter_steer	trained checkpoints in NoThink mode with their own training drift along v_leak removed at layer 20 (dose 1)	15

Every (config, model, method, step, thinking_mode, dose) combination holds exactly 120 x 16 = 1,920 rollouts.

from datasets import load_dataset
ds = load_dataset("zml5418/thinking-leakage-rollouts", "GRPO", split="test")
ds = ds.filter(lambda r: r["model"] == "Qwen3-8B" and r["step"] == 500 and r["thinking_mode"] == "nothink")

Fields

field	meaning
model	Qwen3-8B, Qwen3-4B or MiniCPM4.1-8B
method	base, GRPO, OPSD, OPSD-LoRA or SFT
step	training step of the checkpoint (0 for the base model). For counter_steer, method + step name the checkpoint that was intervened on; its un-intervened rollouts are in that method's config
thinking_mode	think or nothink (chat template enable_thinking true / false)
condition	bare (no intervention), steer or counter_steer
dose	steer: alpha, in units of the base model's Think-NoThink gap. counter_steer: gamma (always 1). Null for bare
qid	our row index (0-119) into the combined 120-problem test set; internal, not part of any original release
source_dataset	original release the problem comes from (see below)
source_id	the problem's id in that release
problem	problem statement as released
ground_truth	reference answer
rollout_idx	sample index, 0-15
response	the generated text (prompt excluded)
pred	answer extracted from the last \boxed{}
is_correct	graded correct (math-equivalence check, as used for every number in the paper)

Problems

The test set concatenates four releases, 30 problems each. To match a row to its original problem, use source_dataset + source_id:

qid	source_dataset	source_id
0-29	math-ai/aime24	id (60-89)
30-59	math-ai/aime25	id (0-29)
60-89	MathArena/hmmt_feb_2025	problem_idx (1-30)
90-119	MathArena/hmmt_nov_2025	problem_idx (1-30)

The user message sent to every model was problem followed by "\n\nPlease reason step by step, and put your final answer within \\boxed{}.", rendered with each model's own chat template.

Citation

If you find this dataset useful, please cite:

@misc{liu2026thinkingleakagecausalaudit,
      title={Thinking Leakage: A Causal Audit of NoThink Post-Training in Hybrid Reasoning Models},
      author={Zehao Liu and Vasant G. Honavar},
      year={2026},
      eprint={2609.28682},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2609.28682},
}

The problems belong to their original releases; see those for their terms.