Training dataset and LoRA checkpoints for the arXiv 2026 preprint Controllable Reasoning Models are Private Thinkers
Haritz Puerto
haritzpuerto
AI & ML interests
Reasoning in LLMs, trustworthy AI, agents
Recent Activity
updated
a collection
about 3 hours ago
⚙️🧠🔒 Controllable Reasoning Models are Private Thinkers updated
a dataset about 3 hours ago
haritzpuerto/controlling-reasoning-models-privacy-outputs published
a dataset about 3 hours ago
haritzpuerto/controlling-reasoning-models-privacy-outputs Organizations
Models from the ACL 2025 paper "Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs"
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Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language Models
Paper • 2407.03181 • Published • 1 -
haritzpuerto/LLaMA2-7B-dcot
Text Generation • Updated • 2 • 2 -
haritzpuerto/LLaMA2-13B-dcot
Text Generation • Updated • 1 -
haritzpuerto/LLaMA2-70B-dcot
Text Generation • Updated • 1
Training dataset and LoRA checkpoints for the arXiv 2026 preprint Controllable Reasoning Models are Private Thinkers
Models from the ACL 2025 paper "Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs"
"
-
Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language Models
Paper • 2407.03181 • Published • 1 -
haritzpuerto/LLaMA2-7B-dcot
Text Generation • Updated • 2 • 2 -
haritzpuerto/LLaMA2-13B-dcot
Text Generation • Updated • 1 -
haritzpuerto/LLaMA2-70B-dcot
Text Generation • Updated • 1
Samples used for the NAACL 2025 Findings paper: "Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models."