title: Tina: Tiny Reasoning Models via LoRA
summary: How cost-effectively can strong reasoning abilities be achieved in language
  models? Driven by this fundamental question, we present Tina, a family of tiny
  reasoning models achieved with high cost-efficiency. Notably, Tina demonstrates
  that substantial reasoning performance can be developed using only minimal
  resources, by applying parameter-efficient updates during reinforcement
  learning (RL), using low-rank adaptation (LoRA), to an already tiny 1.5B
  parameter base model. This minimalist approach produces models that achieve
  reasoning performance which is competitive with, and sometimes surpasses, SOTA
  RL reasoning models built upon the same base model. Crucially, this is achieved
  at a tiny fraction of the computational post-training cost employed by existing
  SOTA models. In fact, the best Tina model achieves a >20\% reasoning
  performance increase and 43.33\% Pass@1 accuracy on AIME24, at only \$9 USD
  post-training and evaluation cost (i.e., an estimated 260x cost reduction). Our
  work reveals the surprising effectiveness of efficient RL reasoning via LoRA.
  We validate this across multiple open-source reasoning datasets and various
  ablation settings starting with a single, fixed set of hyperparameters.
  Furthermore, we hypothesize that this effectiveness and efficiency stem from
  LoRA rapidly adapting the model to the structural format of reasoning rewarded
  by RL, while largely preserving the base model's underlying knowledge. In
  service of accessibility and open research, we fully open-source all code,
  training logs, and model weights \& checkpoints.
paper_authors._id: 6808452d16c1c427ac727816
paper_authors.name: Shangshang Wang
paper_authors.status: claimed_verified
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paper_authors.fullname: Shangshang Wang
paper_authors.type: user
paper_authors.user: upup-ashton-wang
paper_authors._id: 6808452d16c1c427ac727817
paper_authors.name: Julian Asilis
paper_authors.status: claimed_verified
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paper_authors.fullname: Julian Asilis
paper_authors.type: user
paper_authors.user: asil012
paper_authors._id: 6808452d16c1c427ac727818
paper_authors.name: Ömer Faruk Akgül
paper_authors.status: claimed_verified
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paper_authors.fullname: Omer Faruk Akgul
paper_authors.type: user
paper_authors.user: farukakgul
paper_authors._id: 6808452d16c1c427ac727819
paper_authors.name: Enes Burak Bilgin
paper_authors.status: claimed_verified
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paper_authors.fullname: Enes Burak Bilgin
paper_authors.type: user
paper_authors.user: ebbilgin
paper_authors._id: 6808452d16c1c427ac72781a
paper_authors.name: Ollie Liu
paper_authors._id: 6808452d16c1c427ac72781b
paper_authors.name: Willie Neiswanger
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-25T08:35:24.290Z
paper_authors._id: 644bf65522d211df6444a7f4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644bf65522d211df6444a7f4/k_ddZdQDg2fzhwjI1EXyx.jpeg
paper_authors.fullname: Willie Neiswanger
paper_authors.type: user
paper_authors.user: willieneis
paper_title: Tina: Tiny Reasoning Models via LoRA
paper_summary: How cost-effectively can strong reasoning abilities be achieved in language
  models? Driven by this fundamental question, we present Tina, a family of tiny
  reasoning models achieved with high cost-efficiency. Notably, Tina demonstrates
  that substantial reasoning performance can be developed using only minimal
  resources, by applying parameter-efficient updates during reinforcement
  learning (RL), using low-rank adaptation (LoRA), to an already tiny 1.5B
  parameter base model. This minimalist approach produces models that achieve
  reasoning performance which is competitive with, and sometimes surpasses, SOTA
  RL reasoning models built upon the same base model. Crucially, this is achieved
  at a tiny fraction of the computational post-training cost employed by existing
  SOTA models. In fact, the best Tina model achieves a >20\% reasoning
  performance increase and 43.33\% Pass@1 accuracy on AIME24, at only \$9 USD
  post-training and evaluation cost (i.e., an estimated 260x cost reduction). Our
  work reveals the surprising effectiveness of efficient RL reasoning via LoRA.
  We validate this across multiple open-source reasoning datasets and various
  ablation settings starting with a single, fixed set of hyperparameters.
  Furthermore, we hypothesize that this effectiveness and efficiency stem from
  LoRA rapidly adapting the model to the structural format of reasoning rewarded
  by RL, while largely preserving the base model's underlying knowledge. In
  service of accessibility and open research, we fully open-source all code,
  training logs, and model weights \& checkpoints.
paper_ai_summary: Tina, a family of tiny reasoning models, achieves high reasoning performance at minimal computational cost through LoRA during reinforcement learning.
paper_ai_keywords: reinforcement learning LoRA parameter-efficient updates