eledaveri/ComputerVision

This is an academic reference implementation accompanying a research paper comparing SFT vs. RL post-training strategies. With zero stars, forks, and velocity over 420 days, it has no user adoption or community traction. The contribution is methodological (a comparative study) rather than a reusable tool or framework. Platform Domination Risk (HIGH): OpenAI, Anthropic, Meta, and Google are actively researching and deploying SFT and RLHF techniques. The findings here (if novel) will likely be absorbed into their own model training pipelines within 1-2 years. Anthropic's Constitutional AI, OpenAI's RLHF work, and similar initiatives at Gemini/LLaMA teams directly subsume this research space. Market Consolidation Risk (MEDIUM): The research question itself (SFT vs. RL trade-offs) is central to foundation model development, but the implementation is not a product. It could be acquired as IP if the findings are sufficiently novel, but the repo shows no evidence of being the primary reference—academic citation matters more than GitHub adoption here. Displacement Horizon (1-2 YEARS): Within 2 years, platform providers will have published their own definitive guidance on SFT vs. RL based on larger-scale experiments. This repo's value diminishes as soon as competing implementations (from larger labs) publish similar findings with more compute. Composability: This is an algorithm/methodology paper with accompanying code, not a component library. It's meant to be cited and reproduced, not imported into other projects. Implementation Depth: Reference implementation—works but not production-hardened or meant for real-world deployment. Novelty: Novel combination (comparing two well-known post-training approaches in a structured way), but the core techniques (SFT, RL, RLHF) are established.