8块钱跑通一次强化学习全流程，潞晨云重塑微调赛道：1名算法工程师=1支Infra团队

Core Viewpoint - The article discusses the shift in large model training from "violent pre-training" to "post-training," emphasizing the importance of fine-tuning and reinforcement learning (RL) in enhancing model performance [1][2]. Group 1: Post-Training and Reinforcement Learning - The industry consensus is that breakthroughs in large model capabilities now rely more on post-training, particularly RL, rather than solely on pre-training parameter accumulation [7]. - DeepSeek-R1's performance improvement in AIME mathematical reasoning benchmark, with pass@1 increasing from 15.6% to 77.9% through RL, exemplifies the potential of RL in achieving significant capability leaps with limited data [7]. Group 2: Challenges in Algorithm Engineering - Algorithm engineers face significant challenges due to complex distributed infrastructure, high GPU rental costs, and intricate architecture tuning, which hinder access to advanced training environments [3][9]. - The introduction of Tinker aims to simplify the training process by providing a standard API, decoupling algorithm design from infrastructure, allowing developers to focus on data and loss function definitions [10]. Group 3: Efficiency and Cost Structure - The Luchenchun Fine-Tuning SDK allows a single algorithm engineer to replace a large infrastructure team, significantly enhancing productivity by simplifying the training process [12][16]. - The SDK's serverless architecture introduces a "pay-per-token" billing model, which charges users only for effective computation tokens used during prefill, sample, and training, eliminating costs associated with idle GPU time [26][29]. Group 4: Practical Applications and User Experience - The SDK supports various use cases, including academic research, startup MVP validation, and industrial applications, enabling users to conduct experiments without the burden of resource management [32][35][37]. - Users can easily train large models using familiar Python syntax, with the SDK providing a seamless experience from installation to execution, thus lowering the barrier to entry for complex model training [39][41]. Group 5: Future of AI Infrastructure - The ultimate goal of AI infrastructure is to achieve "zero cognitive load," where developers only need to describe data and algorithms, while all operational complexities are managed by the system [42]. - As GPU idle costs approach zero and environment setup times decrease, the efficiency of application innovation will be maximized, pushing the limits of computational capabilities [43].