RPS: Information Elicitation with Reinforcement Prompt Selection

RPS (Reinforcement Prompt Selection) is a research-oriented project aimed at solving a specific bottleneck in LLM interactions: getting users to provide necessary but withheld information. While the research approach of using RL to optimize for information disclosure is sound, the project currently lacks any significant moat. With 0 stars and being only 2 days old, it serves primarily as a reference implementation for a paper (likely a 2024/2025 release despite the ArXiv typo in the source). From a competitive standpoint, this functionality is highly susceptible to platform domination. Frontier labs (OpenAI, Anthropic) are already incorporating 'proactivity' and 'clarification' goals into their RLHF/RLAIF pipelines. An external prompt-selection layer is a workaround for model-level limitations that are rapidly being addressed. Furthermore, prompt optimization frameworks like DSPy or LangSmith provide more generalized toolsets for achieving similar outcomes. The 8 forks suggest early academic peer interest, but without a robust library architecture or proprietary dataset, it remains a reproducible research artifact rather than a defensible software product. Its utility will likely be absorbed into the system prompts or fine-tuning objectives of the next generation of base models within 6 months.