Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning
arXiv:2603.09803v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally, potentially reinforcing flawed traces that arrive at correct answers by chance. We observe that \emph{better reasoning makes better demonstrations}: high-quality solutions serve as more effective in-context examples than low-quality ones. We term this teaching ability \textbf{Demonstration Utility}, and show that the policy model's own in-context learning ability provides an efficient way to measure it, y
This research builds on recent advances in self-supervised learning and in-context learning within large language models, addressing the critical need for improved reasoning quality rather than merely correct answers.
Improving the intrinsic quality of reasoning in large language models via better demonstration selection will lead to more robust, reliable, and trustworthy AI systems, expanding their potential applications and accelerating AI development.
The focus in AI development shifts from purely 'correct answers' to 'high-quality reasoning paths,' enabling more efficient and effective training of advanced AI models.
- · AI algorithm developers
- · Large language model providers
- · AI-powered solution companies
- · Researchers in AI safety and alignment
- · Developers of less sophisticated 'brute-force' AI approaches
AI models will exhibit more robust and explainable decision-making processes.
This improved reasoning ability will make AI agents more capable of handling complex, real-world tasks with higher reliability.
The enhanced trustworthiness and capability of AI could accelerate the deployment of autonomous systems across various industries, including for critical infrastructure.
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