arXiv:2509.06948v3 Announce Type: replace Abstract: Supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) are two widely used post-training paradigms for improving the reasoning ability of large language models (LLMs). Recent methods attempt to integrate SFT and RLVR in a single stage by reweighting or scheduling their objectives. However, such coupling can be counterproductive because supervised updates are not uniformly beneficial for reward optimization. To address this, we propose BRIDGE, a scalable framework in which SFT learns to supervise RL by selective
The continuous evolution of LLM training paradigms is a critical area of research, and 'replace' announcements like this signify advancements in core methodologies.
Improved LLM reasoning capabilities through more effective training methods directly enhance the performance and utility of AI systems, potentially accelerating their deployment in complex tasks.
The proposed BRIDGE framework suggests a more efficient and less counterproductive integration of SFT and RL, potentially leading to more robust and scalable LLM development.
- · AI researchers
- · LLM developers
- · Companies deploying AI models
- · Developers relying on less efficient training methods
- · Systems with lower reasoning power
LLMs achieve higher reasoning accuracy and efficiency across various benchmarks and applications.
Accelerated development and adoption of AI systems capable of more sophisticated problem-solving.
Increased competition among foundation model providers to integrate advanced training techniques, leading to a new wave of benchmark performance improvements.
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