On Data Engineering for Scaling LLM Terminal Capabilities
Paper
• 2602.21193 • Published
• 89
John Posada
elchulito89
AI & ML interests
None yet
Recent Activity
upvoted a paper 5 days ago
On Data Engineering for Scaling LLM Terminal Capabilities liked
a dataset 6 days ago
applied-ai-018/Coding reacted
to
ajibawa-2023's
post with 🔥 6 days ago
PHP-Code-Large
Dataset: https://huggingface.co/datasets/ajibawa-2023/PHP-Code-Large
PHP-Code-Large is a large-scale corpus of PHP source code comprising more than 12 million lines of PHP code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the PHP ecosystem.
By providing a high-volume, language-specific corpus, PHP-Code-Large enables systematic experimentation in PHP-focused model training, domain adaptation, and downstream code understanding tasks.
PHP-Code-Large addresses the need for a dedicated PHP-only dataset at substantial scale, enabling focused research across backend systems, CMS platforms, APIs, and full-stack PHP environments. Organizations
None yet
upvoted a paper 5 days ago
liked a dataset 6 days ago
reacted to ajibawa-2023's post with 🔥🚀👍 6 days ago
Post
2521
PHP-Code-Large
Dataset: ajibawa-2023/PHP-Code-Large
PHP-Code-Large is a large-scale corpus of PHP source code comprising more than 12 million lines of PHP code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the PHP ecosystem.
By providing a high-volume, language-specific corpus, PHP-Code-Large enables systematic experimentation in PHP-focused model training, domain adaptation, and downstream code understanding tasks.
PHP-Code-Large addresses the need for a dedicated PHP-only dataset at substantial scale, enabling focused research across backend systems, CMS platforms, APIs, and full-stack PHP environments.
Dataset: ajibawa-2023/PHP-Code-Large
PHP-Code-Large is a large-scale corpus of PHP source code comprising more than 12 million lines of PHP code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the PHP ecosystem.
By providing a high-volume, language-specific corpus, PHP-Code-Large enables systematic experimentation in PHP-focused model training, domain adaptation, and downstream code understanding tasks.
PHP-Code-Large addresses the need for a dedicated PHP-only dataset at substantial scale, enabling focused research across backend systems, CMS platforms, APIs, and full-stack PHP environments.
Was the 32B model trained on 25K or 200K Trajectories?
3
#2 opened about 1 month ago
by
elchulito89
upvoted a paper about 1 month ago
liked a model about 1 month ago
upvoted 2 papers about 1 month ago
reacted to cjerzak's post with 🔥 about 1 month ago
Post
691
Ethos: In our team at UT Austin, we train students to become full-stack researchers—and increasingly, designers of the systems that do research. Our students learn to carry projects end-to-end: from idea generation and theory to data creation, analysis, and iterative refinement across diverse subfields. Using modern AI (including agentic workflows) and scalable computation, students build reproducible pipelines that can ingest and update planetary-scale data—like satellite imagery and other high-dimensional sources. But the goal isn’t tool use for its own sake: students learn to set the objectives, constraints, and evaluation standards that guide these systems through large spaces of hypotheses, while grounding results in causal inference and careful measurement. The outcome is scholarship that can rigorously test policy counterfactuals and translate evidence into durable, responsible improvements in societal well-being.
We welcome students at every stage to engage with projects—from motivated high-schoolers to undergraduates, graduate students, and those from highly non-traditional backgrounds.
Join us! https://connorjerzak.com/students/
We welcome students at every stage to engage with projects—from motivated high-schoolers to undergraduates, graduate students, and those from highly non-traditional backgrounds.
Join us! https://connorjerzak.com/students/
- 2 replies
liked a model about 1 month ago
upvoted 3 papers about 1 month ago
Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs
Paper
• 2601.08763 • Published
• 148
DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset
Paper
• 2601.10305 • Published
• 36
Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering
Paper
• 2601.10402 • Published
• 37
liked a model about 2 months ago
upvoted a paper about 2 months ago
upvoted 2 papers 2 months ago
reacted to AdinaY's post with 🔥 2 months ago
Post
4630
Finch 💰 an enterprise-grade benchmark that measures whether AI agents can truly handle real world finance & accounting work.
FinWorkBench/Finch
✨ Built from real enterprise data (Enron + financial institutions), not synthetic tasks
✨ Tests end-to-end finance workflows
✨ Multimodal & cross-file reasoning
✨ Expert annotated (700+ hours) and genuinely challenging hard
FinWorkBench/Finch
✨ Built from real enterprise data (Enron + financial institutions), not synthetic tasks
✨ Tests end-to-end finance workflows
✨ Multimodal & cross-file reasoning
✨ Expert annotated (700+ hours) and genuinely challenging hard