analiza aiqbench
This research proposes a framework where large language models autonomously generate training data and validate knowledge through environmental interaction, moving beyond human-derived datasets to address model collapse. By utilizing unbounded numeric rewards like disk space or follower counts, the system enables self-retraining via code synthesis and GRPO fine-tuning without relying on traditional textual benchmarks. The work outlines a significant pathway toward autonomous general intelligence by allowing AI agents to iteratively improve their own capabilities through empirical validation.
Generalising from Self-Produced Data: Model Training Beyond Human Constraints
arXiv:2504.04711v2 Announce Type: replace Abstract: Current large language models (LLMs) are constrained by human-derived training data and limited by a single level of abstraction that impedes definitive truth judgments. This paper introduces a novel framework in which AI models autonomously generate and validate new knowledge through direct interaction with their environment. Central to this approach is an unbounded, ungamable numeric reward - such as annexed disk space or follower count - that guides learning without requiring human benchmarks. AI agents iteratively generate strategies and executable code to maximize this metric, with successful outcomes forming the basis for self-retraining and incremental generalisation. To mitigate model collapse and the warm start problem, the framework emphasizes empirical validation over textual similarity and supports fine-tuning via GRPO. The system architecture employs modular agents for environment analysis, strategy generation, and code synthesis, enabling scalable experimentation. This work outlines a pathway toward self-improving AI systems capable of advancing beyond human-imposed constraints toward autonomous general intelligence.
Abstract:Current large language models (LLMs) are constrained by human-derived training data and limited by a single level of abstraction that impedes definitive truth judgments. This paper introduces a novel framework in which AI models autonomously generate and validate new knowledge through direct interaction with their environment. Central to this approach is an unbounded, ungamable numeric reward - such as annexed disk space or follower count - that guides learning without requiring human benchmarks. AI agents iteratively generate strategies and executable code to maximize this metric, with successful outcomes forming the basis for self-retraining and incremental generalisation. To mitigate model collapse and the warm start problem, the framework emphasizes empirical validation over textual similarity and supports fine-tuning via GRPO. The system architecture employs modular agents for environment analysis, strategy generation, and code synthesis, enabling scalable experimentation. This work outlines a pathway toward self-improving AI systems capable of advancing beyond human-imposed constraints toward autonomous general intelligence.
Artificial Intelligence (cs.AI)
arXiv:2504.04711 [cs.AI]
(or arXiv:2504.04711v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2504.04711 Focus to learn more arXiv-issued DOI via DataCite
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