Cap prompt generation at 512 tokens and add version print

Add SFT warm start before GRPO and DB connectivity init check

Add local model inference backend for Layer 2

Make Supabase uploads incremental — upload after every step

Add Supabase upload for training results (Storage + DB)

Add raw training summary output and adjust training scale

Add volume verification, fsync, and stdout fallback for training outputs

Clean up dead code, unused imports, and move hardcoded values to config.yaml

Add --llm-agent and other legacy CLI flags for backwards compatibility

Centralize all training params in config.yaml (single source of truth)

Remove mock mode: only real GRPO RL training remains

Add clear training progress logging with technical + domain names

Update docstrings to reflect LLM-only training pipeline

Align GRPOConfig defaults with CLI: 10 steps, 7 episodes

Remove all rule-based fallback systems, require LLM inference

Reduce training defaults for fast iteration: steps=10, episodes=7

Add training report & logging system with reward charts and conversation comparisons

Wire up real LLM integration via HF Inference API

Fix critical gaps: prompt-sensitive agent, adversarial customers, executable GRPO, OpenEnv wrapper