## Flowchart: Multi-Stage AI Model Development Pipeline ### Overview The diagram illustrates a three-stage pipeline for developing an AI model, starting from base model fine-tuning to group arena reward optimization. It combines technical components (SFT data, reward services) with reinforcement learning frameworks (Evo-CoT, Group Arena Reward) to produce a final instruction-tuned model. ### Components/Axes **Stage 1: Decoupled Fine-Tuning** - Input: SFT Data (Reasoning, General, Industrial Problems) - Process: - SysPrompt 1 → Instant Response - SysPrompt 2 → In-depth Reasoning - Output: Decoupled Fine-Tuning **Stage 2: Evolutional RL (Evo-CoT)** - Input: Policy from Stage 1 - Process: - Diversified Reasoning tasks → Initial Attempt → Self-reflection → Deeper reasoning - Reward Envs (Correctness, Brevity, Format, Visual) - Output: Policy with refined reasoning **Stage 3: RL with Group Arena Reward** - Input: Policy from Stage 2 - Process: - RubriX evaluation with Checklist (Conciseness, ...) - Group Arena Reward calculation - Output: Final policy with group-based optimization ### System Components - Reward Service (Batch/Stream) - Async Dispatch Queue - High Scalability (40K+ Concurrency) - Reward Policy (Rule/Model-Based) ### Detailed Analysis **Stage 1 Components** - SFT Data categories: - Reasoning: math, stem, code, logic - General: creative writing, social sciences - Industrial: finance, optimization - Decoupled Fine-Tuning shows two response modes: - SysPrompt 1: Instant Response - SysPrompt 2: In-depth Reasoning **Stage 2 Components** - Evo-CoT process: - Initial Attempt → Self-reflection → Deeper reasoning - Reward dimensions: Correctness, Brevity, Format, Visual - Policy refinement shown through iterative attempts **Stage 3 Components** - RubriX evaluation with: - Checklist items (Conciseness: 0.15, ...) - Group Arena Reward calculation - Visual representation of reward distribution across outputs ### Key Observations 1. Progressive complexity: Starts with basic fine-tuning → adds self-reflection → ends with group-based optimization 2. Reward system evolution: From individual metrics (Stage 2) to group-based evaluation (Stage 3) 3. Scalability emphasis: Explicit mention of 40K+ concurrency in reward service 4. Technical integration: Combines SFT data with RL frameworks ### Interpretation This pipeline demonstrates a sophisticated approach to AI model development that: 1. Begins with specialized fine-tuning on diverse data categories 2. Implements evolutionary reinforcement learning with self-reflection mechanisms 3. Culminates in group-based reward optimization using RubriX evaluation 4. Maintains high scalability throughout the process The architecture suggests a focus on creating models that can handle complex reasoning tasks while maintaining efficiency and adaptability. The group arena reward system implies a competitive or collaborative training environment that could enhance model robustness through peer evaluation.