mlsanityData to 13 Oct 2026

Rethinking Supervised Fine-Tuning: Emphasizing Key Answer Tokens for Improved LLM Accuracy

arXiv 2512.21017 ↗First posted 24 Dec 2025cs.CL cs.AIcited by 4

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

With the rapid advancement of Large Language Models (LLMs), the Chain-of-Thought (CoT) component has become significant for complex reasoning tasks. However, in conventional Supervised Fine-Tuning (SFT), the model could allocate disproportionately more attention to CoT sequences with excessive length. This reduces focus on the much shorter but essential Key portion-the final answer, whose correctness directly determines task success and evaluation quality. To address this limitation, we propose SFTKey, a two-stage training scheme. In the first stage, conventional SFT is applied to ensure proper output format, while in the second stage, only the Key portion is fine-tuned to improve accuracy. Extensive experiments across multiple benchmarks and model families demonstrate that SFTKey achieves an average accuracy improvement exceeding 5% over conventional SFT, while preserving the ability to generate correct formats. Overall, this study advances LLM fine-tuning by explicitly balancing CoT learning with additional optimization on answer-relevant tokens.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 17 of its 24 references that are in the map. First posted on arXiv 24 Dec 2025.

Builds on

17 of 24 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models6 May
  2. 2025TokenSkip: Controllable Chain-of-Thought Compression in LLMs17 Feb

Other references · 15

  1. 2023GPT-4 Technical Reportcited by 18,278
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,376
  3. 2021Training Verifiers to Solve Math Word Problemscited by 9,916
  4. 2025Qwen3 Technical Reportcited by 8,510
  5. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,247
  6. 2023From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuningcited by 303
  7. 2023LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Modelscited by 284
  8. 2023The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuningcited by 108
  9. 2025Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuningcited by 39
  10. 2021A Few More Examples May Be Worth Billions of Parameterscited by 19
  11. 2025Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretrainingcited by 16
  12. 2025Token Weighting for Long-Range Language Modelingcited by 14
3 more
  1. 2025Supervised Optimism Correction: Be Confident When LLMs Are Surecited by 5
  2. 2025Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuningcited by 4
  3. 2025SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Modelscited by 2

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026BITS Pilani at SemEval-2026 Task 9: Structured Supervised Fine-Tuning with DPO Refinement for Polarization Detectioncited by 1
  2. 2026AlphaToken: Decoupling Adaptation and Stability for Path-Aware Response Token Valuation in LLM Post-Trainingcited by 1
  3. 2026Supervised Fine-Tuning Needs to Unlock the Potential of Token Prioritycited by 0
  4. 2026ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training
  2. 2023Training Chain-of-Thought via Latent-Variable Inference
  3. 2025Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
  4. 2025CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning
  5. 2025QFFT, Question-Free Fine-Tuning for Adaptive Reasoning
  6. 2024Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs
  7. 2023Making Large Language Models Better Reasoners with Alignment
  8. 2025Enhancing Large Language Model Reasoning via Selective Critical Token Fine-Tuning