arXiv:2605.28010v1 Announce Type: new Abstract: Self-evolving large language models (LLMs) learn by generating their own training tasks and solutions, reducing reliance on human-curated supervision. However, in many reasoning domains, the model must also validate generated tasks and judge generated answers to obtain training signals. This creates a training-signal challenge: erroneous self-judgments become erroneous gradient updates. Existing approaches either rely on external verifiers, which limits generality, or treat noisy self-generated feedback as supervision. We propose COSE (Confidence
The increasing sophistication of LLMs and the recognition of their limitations regarding self-supervision necessitates novel approaches to robust autonomous learning.
Improving the autonomous learning capabilities of LLMs via more reliable feedback mechanisms is critical for scaling AI development without proportionate human intervention.
This research introduces a method for LLMs to generate more reliable training signals internally, potentially reducing reliance on external verifiers and making self-evolution more robust.
- · AI research labs
- · LLM developers
- · Autonomous agent builders
- · Companies reliant on large human annotation teams for model fine-tuning
Increased efficiency and reduced cost in training advanced LLMs capable of self-improvement.
Acceleration in the development of more complex and reliable AI agents and autonomous systems.
Potentially less predictable AI system behavior as models become more self-reliant for their own evolution and validation.
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Read at arXiv cs.AI