Aspire: Can Models Self-Evolve from Vague Goals?
Many important forms of human learning begin with a vague goal, such as "become a better physicist" or "improve at research." Learners must interpret the goal, identify capability gaps, decide how to learn, and determine whether they have actually improved. In contrast, existing work on LLM self-evolution typically begins with tasks and evaluation metrics specified by humans, reducing self-evolution to optimizing an explicit objective rather than deciding what and how to learn. We introduce ASPIRE, a benchmark for vague-goal-driven self-evolution. ASPIRE provides only a natural-language capabi
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- PossiblePossibly related (embedding) · 52%AI’s recursive self-improvement might not come so quickly after all →
- PossiblePossibly related (embedding) · 51%Mirage of Mastery: Memorization Tricks LLMs into Artificially Inflated Self-Knowledge - The Association for the Advancement of Artificial Intelligence →
- PossiblePossibly related (embedding) · 51%AI’s recursive self-improvement might not come so quickly after all - MIT Technology Review →
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- LinkedLinked via arxiv author · 85%Yuhao Wu →
“Aspire: Can Models Self-Evolve from Vague Goals?”