Cover Image

Towards Revealing the Effectiveness of Small-Scale Fine-tuning in R1-style Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Yutong, Gao, Jiandong, Wu, Ji
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

CoScale-RL: Efficient Post-Training by Co-Scaling Data and Computation
by: Chen, Yutong, et al.
Published: (2026)

Dynamic feature selection in medical predictive monitoring by reinforcement learning
by: Chen, Yutong, et al.
Published: (2024)

Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learning
by: Pan, Haolin, et al.
Published: (2025)

Fine-tuning Timeseries Predictors Using Reinforcement Learning
by: Cazaux, Hugo, et al.
Published: (2026)

Silent Sabotage During Fine-Tuning: Few-Shot Rationale Poisoning of Compact Medical LLMs
by: Xie, Jingyuan, et al.
Published: (2026)

AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments
by: Cai, Zhijie, et al.
Published: (2026)

Fine-tuning Behavioral Cloning Policies with Preference-Based Reinforcement Learning
by: Macuglia, Maël, et al.
Published: (2025)

Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps
by: Yang, Ningyuan, et al.
Published: (2025)

Demystifying Design Choices of Reinforcement Fine-tuning: A Batched Contextual Bandit Learning Perspective
by: Xie, Hong, et al.
Published: (2026)

REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning
by: Gu, Zhaoyuan, et al.
Published: (2026)

Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs
by: Chen, Jack, et al.
Published: (2025)

Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
by: Huang, Kai, et al.
Published: (2023)

A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning
by: Gupta, Shashank, et al.
Published: (2025)

On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training
by: Niu, Xueyan, et al.
Published: (2026)

Hierarchical Balance Packing: Towards Efficient Supervised Fine-tuning for Long-Context LLM
by: Yao, Yongqiang, et al.
Published: (2025)

New Insights on Unfolding and Fine-tuning Quantum Federated Learning
by: Nanayakkara, Shanika Iroshi, et al.
Published: (2025)

Mechanistic Fine-tuning for In-context Learning
by: Cho, Hakaze, et al.
Published: (2025)

Skrull: Towards Efficient Long Context Fine-tuning through Dynamic Data Scheduling
by: Xu, Hongtao, et al.
Published: (2025)

A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective
by: Qing, Yunpeng, et al.
Published: (2024)

Kimi k1.5: Scaling Reinforcement Learning with LLMs
by: Kimi Team, et al.
Published: (2025)

State-Novelty Guided Action Persistence in Deep Reinforcement Learning
by: Hu, Jianshu, et al.
Published: (2024)

Scale-free Adversarial Reinforcement Learning
by: Chen, Mingyu, et al.
Published: (2024)

Selecting Large Language Model to Fine-tune via Rectified Scaling Law
by: Lin, Haowei, et al.
Published: (2024)

Outlier-weighed Layerwise Sampling for LLM Fine-tuning
by: Li, Pengxiang, et al.
Published: (2024)

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning
by: Liu, Jia, et al.
Published: (2025)

Adaptive Divergence Regularized Policy Optimization for Fine-tuning Generative Models
by: Fan, Jiajun, et al.
Published: (2025)

Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds
by: Wang, Fan, et al.
Published: (2025)

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models
by: Chen, Xiangyu, et al.
Published: (2025)

Membership Inference Attacks Against Fine-tuned Diffusion Language Models
by: Chen, Yuetian, et al.
Published: (2026)

CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning
by: Kim, Yeachan, et al.
Published: (2024)

Activated LoRA: Fine-tuned LLMs for Intrinsics
by: Greenewald, Kristjan, et al.
Published: (2025)

Rethinking Safety in LLM Fine-tuning: An Optimization Perspective
by: Kim, Minseon, et al.
Published: (2025)

Aggregating Low Rank Adapters in Federated Fine-tuning
by: Trautmann, Evelyn, et al.
Published: (2025)

Speculative Coreset Selection for Task-Specific Fine-tuning
by: Zhang, Xiaoyu, et al.
Published: (2024)

On the Effective Horizon of Inverse Reinforcement Learning
by: Xu, Yiqing, et al.
Published: (2023)

Pentest-R1: Towards Autonomous Penetration Testing Reasoning Optimized via Two-Stage Reinforcement Learning
by: Kong, He, et al.
Published: (2025)

Cog-Rethinker: Hierarchical Metacognitive Reinforcement Learning for LLM Reasoning
by: Sun, Zexu, et al.
Published: (2025)

Skill-R1: Agent Skill Evolution via Reinforcement Learning
by: Vishe, Yash, et al.
Published: (2026)

Tree Search for LLM Agent Reinforcement Learning
by: Ji, Yuxiang, et al.
Published: (2025)

Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks
by: Wang, Zhili, et al.
Published: (2023)