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SBCO: Self-Supervised, Verifier-Grounded Harness Optimization For Planning Agents
SBCO is a new self-supervised, verifier-grounded optimization method for planning agents that improves performance without self-reference or human labels, using significantly less compute than self-modifying baselines.
Aug 12, 2026
Self-Supervised Skill Optimization
Researchers introduced Self-Supervised Skill Optimization (SSO), a method that improves LLM agent skills using only unlabeled task data and an LLM judge—no ground-truth labels, rewards, or external evaluators—demonstrating competitive performance against supervised methods.
Aug 3, 2026
Sentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning
A new self-supervised NLP framework called Sentence Splitter uses a T5-based architecture to automatically identify head-tail factual structures in sentences without manual annotation, enabling scalable construction of structure-aware training data for knowledge-intensive tasks.
Jul 23, 2026