Tag
A paper introduces A-RSI (Adaptive Task Refresh for Self-Improvement), a method enabling zero-data multimodal self-improvement by adaptively refreshing tasks during training without requiring new human-labeled data.
The tweet introduces a research concept called Self-Play Pretraining with Zero Data, where two models start from random initialization and generate pretraining data through self-play on a universal Turing machine.
The article introduces Self-Play Pretraining with Zero Data, a research method where two LLMs start from random initialization and learn through a generator proposing programs for a universal Turing machine, demonstrating how inductive biases can enable general learning.
This paper presents an empirical study on bootstrapping conversational recommender systems using synthetic data generated from non-conversational signals, demonstrating that it outperforms zero-shot and scarce real-data methods in low-resource settings.
LURE introduces a zero-data self-play framework for LLM reasoning that uses a pursuit-evasion game to dynamically adjust task difficulty and provide dense rewards, outperforming baselines across multiple environments.