Next-Gen AI Training Paradigm: From RLVR to Real-World Continuous Learning
Prominent AI researcher Dwarkesh Patel recently proposed that the current mainstream AI training paradigm—reinforcement learning from verifiable rewards (RLVR)—is limited to closed environments with predefined reward functions, unable to handle complex open-world tasks. Patel argues the next generation of AI should transcend RLVR, shifting toward continuous learning in real-world tasks where models accumulate experience during actual deployment and efficiently distill that experience back into their weights, forming a closed-loop evolution.
The core of this shift is 'post-deployment foundation' rather than 'pre-deployment training.' Patel notes that traditional AI models almost stop learning after deployment; future AI should be capable of continuous adaptation, extracting effective signals from every interaction to iterate on itself. This paradigm imposes new requirements on compute scheduling, data privacy, and model ownership, and also provides theoretical support for blockchain-integrated decentralized AI networks.
Key Paths: Grindability, OPSD, and Dreaming Simulated Training
Patel highlights three technical enablers: grindability—the ability of a model to operate long-term in real environments and continuously optimize, rather than being fixed after one training run; on-policy self-distillation (OPSD)—self-distillation from samples generated under the model's own policy, improving generalization and preventing outdated behaviors; and 'dreaming' simulated training—active exploration and trial-and-error in virtual environments, creating a mechanism analogous to human mental rehearsal to accelerate experience internalization.
Together, these approaches aim to make AI learn while working, no longer reliant on pre-labeled data or monolithic simulation feedback. For the crypto industry, OPSD and dreaming's compute demands could spawn new distributed computing markets, while grindable models help build intelligent agents in decentralized autonomous organizations that continuously optimize on-chain governance.
Potential Impact on Web3 and Decentralized AI
Patel's framework has implications for Web3 decentralized AI networks. If AI models require 'post-deployment continuous learning,' their training processes inevitably need dynamic, trusted compute and data sources. Blockchain's immutability and incentive mechanisms can provide the infrastructure: model weight updates can be recorded on-chain, OPSD processes verifiable via smart contracts, and dreaming simulations can be executed on distributed GPU networks. Additionally, decentralized identity and privacy-preserving computation can protect user data involved in real-world tasks.
While this paradigm remains in theoretical exploration, several decentralized AI projects are already looking in similar directions. Patel's views offer more concrete application scenarios for compute aggregators, data markets, and model markets. If the leap from RLVR to OPSD/dreaming is realized, decentralized AI infrastructure could capture significantly more value.