BBO-Pile is the first large-scale open dataset of real optimization trajectories used to train and scale foundation models that imitate black-box optimization methods.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
ACC compiles agent trajectories from search, software engineering, and database tasks into long-context QA examples to train LLMs for direct long-range dependency resolution without tool use at inference.
DeltaPrompts generates 200k synthetic high-divergence reasoning prompts to escape zero-delta saturation in multimodal distillation, yielding up to 15% relative gains on chart, document, and perception benchmarks across multiple settings.
citing papers explorer
-
An Open-Source Training Dataset for Foundation Models for Black-box Optimization
BBO-Pile is the first large-scale open dataset of real optimization trajectories used to train and scale foundation models that imitate black-box optimization methods.
-
ACC: Compiling Agent Trajectories for Long-Context Training
ACC compiles agent trajectories from search, software engineering, and database tasks into long-context QA examples to train LLMs for direct long-range dependency resolution without tool use at inference.
-
DeltaPrompts: Escaping the Zero-Delta Trap in Multimodal Distillation
DeltaPrompts generates 200k synthetic high-divergence reasoning prompts to escape zero-delta saturation in multimodal distillation, yielding up to 15% relative gains on chart, document, and perception benchmarks across multiple settings.