TailSFT: Filtered Fine-Tuning Improves Post-Training Performance
cs.LG, cs.AI
Submitted: 2026-08-26
Updated: 2026-08-26
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs
- Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
- On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training
- Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Kimi K3: Open Frontier Intelligence
- Olmo 3
- Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks