Memorize Theorems, Not Instances: Probing SFT Generalization through Mathematical Reasoning
Fuente:
arXiv
Saved in:
| Main Authors: | Peng, Ruiying, Yang, Mengyu, Lei, Jing, Li, Xiaohui, Wu, Xueyu, Chen, Xinlei |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
VAR-MATH: Probing True Mathematical Reasoning in LLMS via Symbolic Multi-Instance Benchmarks
by: Yao, Jian, et al.
Published: (2025)
by: Yao, Jian, et al.
Published: (2025)
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
by: Chu, Tianzhe, et al.
Published: (2025)
by: Chu, Tianzhe, et al.
Published: (2025)
AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy
by: Liu, Zihan, et al.
Published: (2025)
by: Liu, Zihan, et al.
Published: (2025)
Deeper Thought, Weaker Aim: Understanding and Mitigating Perceptual Impairment during Reasoning in Multimodal Large Language Models
by: Peng, Ruiying, et al.
Published: (2026)
by: Peng, Ruiying, et al.
Published: (2026)
Debunk the Myth of SFT Generalization
by: Lin, Xiaofeng, et al.
Published: (2025)
by: Lin, Xiaofeng, et al.
Published: (2025)
The Pitfalls of Memorization: When Memorization Hurts Generalization
by: Bayat, Reza, et al.
Published: (2024)
by: Bayat, Reza, et al.
Published: (2024)
mSFT: Addressing Dataset Mixtures Overfitting Heterogeneously in Multi-task SFT
by: Koh, Woosung, et al.
Published: (2026)
by: Koh, Woosung, et al.
Published: (2026)
A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning
by: Yoshihara, Hiroshi, et al.
Published: (2025)
by: Yoshihara, Hiroshi, et al.
Published: (2025)
Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedback
by: Lin, Yen-Ting, et al.
Published: (2025)
by: Lin, Yen-Ting, et al.
Published: (2025)
Complementing reinforcement learning with SFT through logit averaging in the post training of LLMs
by: Gan, Xingwei, et al.
Published: (2026)
by: Gan, Xingwei, et al.
Published: (2026)
Re-understanding Graph Unlearning through Memorization
by: Ding, Pengfei, et al.
Published: (2026)
by: Ding, Pengfei, et al.
Published: (2026)
SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning
by: Limozin, Alexis, et al.
Published: (2026)
by: Limozin, Alexis, et al.
Published: (2026)
Auditing Reasoning-Trace Memorization Claims after Unlearning with Head-Conditioned Canaries
by: Li, Yanhang, et al.
Published: (2026)
by: Li, Yanhang, et al.
Published: (2026)
DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections
by: Shin, Haebin, et al.
Published: (2025)
by: Shin, Haebin, et al.
Published: (2025)
Memorization vs. Reasoning: Updating LLMs with New Knowledge
by: Li, Aochong Oliver, et al.
Published: (2025)
by: Li, Aochong Oliver, et al.
Published: (2025)
Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead
by: Kang, Feiyang, et al.
Published: (2025)
by: Kang, Feiyang, et al.
Published: (2025)
MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning
by: Li, Xuchen, et al.
Published: (2026)
by: Li, Xuchen, et al.
Published: (2026)
Climbing the Ladder of Reasoning: What LLMs Can-and Still Can't-Solve after SFT?
by: Sun, Yiyou, et al.
Published: (2025)
by: Sun, Yiyou, et al.
Published: (2025)
Beyond Frequency: The Role of Redundancy in Large Language Model Memorization
by: Zhang, Jie, et al.
Published: (2025)
by: Zhang, Jie, et al.
Published: (2025)
Critical Windows of Complexity Control: When Transformers Decide to Reason or Memorize
by: Ali, Sarwan
Published: (2026)
by: Ali, Sarwan
Published: (2026)
Fast and Lightweight Backdoor Detection via Head Random Probing
by: Yu, Yinbo, et al.
Published: (2026)
by: Yu, Yinbo, et al.
Published: (2026)
Metis-RISE: RL Incentivizes and SFT Enhances Multimodal Reasoning Model Learning
by: Qiu, Haibo, et al.
Published: (2025)
by: Qiu, Haibo, et al.
Published: (2025)
Memorization Sinks: Isolating Memorization during LLM Training
by: Ghosal, Gaurav R., et al.
Published: (2025)
by: Ghosal, Gaurav R., et al.
Published: (2025)
Memorization in deep learning: A survey
by: Wei, Jiaheng, et al.
Published: (2024)
by: Wei, Jiaheng, et al.
Published: (2024)
Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration
by: Zhao, Yang, et al.
Published: (2026)
by: Zhao, Yang, et al.
Published: (2026)
Quantization Meets Reasoning: Exploring and Mitigating Degradation of Low-Bit LLMs in Mathematical Reasoning
by: Li, Zhen, et al.
Published: (2025)
by: Li, Zhen, et al.
Published: (2025)
Gradients Must Earn Their Influence: Unifying SFT with Generalized Entropic Objectives
by: Wang, Zecheng, et al.
Published: (2026)
by: Wang, Zecheng, et al.
Published: (2026)
RL Fine-Tuning Heals OOD Forgetting in SFT
by: Jin, Hangzhan, et al.
Published: (2025)
by: Jin, Hangzhan, et al.
Published: (2025)
How Do Flow Matching Models Memorize and Generalize in Sample Data Subspaces?
by: Gao, Weiguo, et al.
Published: (2024)
by: Gao, Weiguo, et al.
Published: (2024)
CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledge
by: Zan, Lei, et al.
Published: (2025)
by: Zan, Lei, et al.
Published: (2025)
Decoding Generalization from Memorization in Deep Neural Networks
by: Ketha, Simran, et al.
Published: (2025)
by: Ketha, Simran, et al.
Published: (2025)
FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
by: Yu, Zhouliang, et al.
Published: (2025)
by: Yu, Zhouliang, et al.
Published: (2025)
Analyzing Memorization in Large Language Models through the Lens of Model Attribution
by: Menta, Tarun Ram, et al.
Published: (2025)
by: Menta, Tarun Ram, et al.
Published: (2025)
Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning
by: Yu, Erxin, et al.
Published: (2025)
by: Yu, Erxin, et al.
Published: (2025)
REFACTOR: Learning to Extract Theorems from Proofs
by: Zhou, Jin Peng, et al.
Published: (2024)
by: Zhou, Jin Peng, et al.
Published: (2024)
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
by: Liu, Zhihan, et al.
Published: (2024)
by: Liu, Zhihan, et al.
Published: (2024)
Implicit Reward as the Bridge: A Unified View of SFT and DPO Connections
by: Wang, Bo, et al.
Published: (2025)
by: Wang, Bo, et al.
Published: (2025)
GAC: Noise-Aware Adaptive Mixing for Hybrid SFT-RL Post-Training
by: Hu, Yuelin, et al.
Published: (2026)
by: Hu, Yuelin, et al.
Published: (2026)
Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling
by: Rodkin, Ivan, et al.
Published: (2025)
by: Rodkin, Ivan, et al.
Published: (2025)
Advanced Weakly-Supervised Formula Exploration for Neuro-Symbolic Mathematical Reasoning
by: Wu, Yuxuan, et al.
Published: (2025)
by: Wu, Yuxuan, et al.
Published: (2025)
Similar Items
-
VAR-MATH: Probing True Mathematical Reasoning in LLMS via Symbolic Multi-Instance Benchmarks
by: Yao, Jian, et al.
Published: (2025) -
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
by: Chu, Tianzhe, et al.
Published: (2025) -
AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy
by: Liu, Zihan, et al.
Published: (2025) -
Deeper Thought, Weaker Aim: Understanding and Mitigating Perceptual Impairment during Reasoning in Multimodal Large Language Models
by: Peng, Ruiying, et al.
Published: (2026) -
Debunk the Myth of SFT Generalization
by: Lin, Xiaofeng, et al.
Published: (2025)