Multimodal Reinforcement Learning Project (MVP Goals)

Multimodal Group – MVP Specification

Project version: v0.1
Repository: involutionhell

1. Vision

Build a lightweight multimodal understanding and generation system that enables the model to interpret images, retrieve relevant information, and produce logically coherent text output.
The goal is to close the full loop from visual perception to language expression, and further develop the ability to explain answers through generated images.

2. MVP Phase Goals

Phase 1: Basic Multimodal Pipeline

Image content recognition (objects, scenes, semantic labels).
Semantic retrieval (image → text / text → image).
Generative understanding and text output.
Model references: CLIP / SigLIP / BLIP-2 / LLaVA / Qwen-VL.

Phase 2: Multimodal Reinforcement Learning

Incorporate user feedback and reward signals to optimise model generation and retrieval performance.
Main directions:
1. RLHF / DPO fine-tuning to learn user preferences.
2. Retrieval strategy optimisation based on behavioural data.
3. Generation quality control and consistency improvement.
Goal: give the system the ability to self-improve and adapt to user preferences.

Phase 2.5: Answer-to-Image Generation

Automatically generate illustrative images from the model's text answers to aid comprehension.
Implementation: use Stable Diffusion / SDXL to convert answer text into image prompts.
Application examples:
- Answer "the process of black hole formation" → generate a structural diagram.
- Explain a scene from a novel → generate a conceptual illustration.
Goal: enable the system not only to understand images and answer questions, but also to explain answers through generated images.

3. System Architecture

[Frontend] → Upload image / Display results
      ↓
[Backend API] → FastAPI + LangChain + Vector Search
      ↓
[Multimodal Models] → CLIP / BLIP / LLaVA / Qwen-VL
      ↓
[RL Module + Answer-to-Image] (Phase 2 and 2.5)

4. Milestones

Phase	Goal	Deliverables
Phase 1	Multimodal recognition and generation	Image recognition, retrieval, text generation
Phase 2	Reinforcement learning optimisation	RLHF / DPO, retrieval strategy optimisation
Phase 2.5	Answer-to-image generation	Automatic illustration generation
Phase 3	Scaling and deployment	Web demo and API interface

5. Team Responsibilities

Module	Owner
Image recognition and encoding	Member A
Semantic retrieval and data processing	Member B
Generation module and model integration	Member C
Reinforcement learning and visualisation output	Member D

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