ColQwen3.5-v2 tops ViDoRe with leaner training

This is a meaningful retrieval-model iteration: less training complexity, slightly better benchmark outcomes, and a clearer recipe others can reproduce.

• –The v2 recipe cuts phases from 4 to 2 while still improving top-line metrics.
• –Domain-heavy data (finance and tables) being included from the start appears to improve real-world document coverage.
• –The 55/45 soup with v1 suggests practical gains can come from checkpoint engineering, not just bigger base models.
• –Apache 2.0 licensing and published weights make it immediately usable for open retrieval stacks.