arXiv (All)
open access
Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training
Mid-training, the stage between pre-training and alignment, is where a model's per-domain data composition is typically set by data availability rather than principled design. We ask what that decision buys, and whether a later alignment pass can undo it. In a controlled logical-reasoning setting (Q
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Domain Mixture Design via Log-Likelihood Differences for Aligning Language Models with a Target Model#799978
- Beyond Surface Forms: Symbolic Edits as a Test for Logical Reasoning with LLMs#799472
- Beyond Surface Forms: Symbolic Edits as a Test for Logical Reasoning with LLMs#798049
- Tasks over Application Manuals: Revealing Gaps in Long-Horizon Procedural Reasoning for Language Models#1070513
- Breaking the Reasoning Horizon in Entity Alignment Foundation Models#820345
Record · ID 986684
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive