Reorganized the Godot 4 coding model direction from simple Q&A learning to a SWE‑agent trajectory learning perspective
Determined that small instruction Q&A alone cannot handle project‑level requests such as “make a map”
Summarized that a real coding agent needs a trajectory that includes repository navigation, relevant file selection, code modification, testing/validation, and patch generation
Recorded related keywords Long-context repository-level software engineering agent training, SWE-agent trajectory training
Documented the entire Godot LLM development roadmap with images and text
Structured the overall flow as data -> first‑stage RAG chatbot -> SFT -> DPO -> SWE Agent
Divided the process from Stage 0 preparation to Stage 6 continuous improvement, covering data collection/structuring, first‑stage RAG chatbot, data labeling, model training, SWE Agent development, and operation/re‑training
The core idea is to first make the first‑stage RAG chatbot a Godot documentation expert, use that chatbot to label/process GitHub data, and then expand to model training and the SWE Agent
Added a note on the Godot RAG judge‑based data generation architecture
Organized it so that the LLM does not make the final label decision; labeling and verification are handled by the system pipeline
Designed the LLM to serve only as a generation assistant for modified code, explanations, SFT Q&A, DPO bad answers, patch drafts, etc.
Recorded the flow from symbol extraction, search, label scoring, to JSONL assembly/validation based on an API‑mapping DB, official‑documentation vector DB, and label‑prototype DB
Categorized the target generated datasets into eight types: version classification, API mapping, migration fix, instruction SFT, DPO preference, repo explorer, patch generation, metadata verification
Summarized the MVP development flow from the Godot RAG judge to the Qwen 3.6 coding model in a separate note
Documented the process from preparing the original godot_docs_full.zip documents, first‑stage chunking with chunk_docs.py, Godot‑specific post‑processing, to building a local search infrastructure
Described the structure where the system decides labels by combining a Vector DB, Keyword Index, Reranker, API Mapping DB, and Label Prototype DB
After structuring GitHub data and running the RAG judge, the LLM’s role is limited to generation assistance such as modified code, explanations, and QA samples
In the first Qwen 3.6 SFT, the goal is to prioritize Godot 4 reasoning, basic GDScript output, and rejection of Godot 3 APIs, then continue with DPO and SWE extensions
Organized GitHub contribution graph handling by setting Git author/email
Changed the global Git config to yyeongjin <appsky1888@gmail.com>
Noted that the existing main history had mixed author/committer emails (local host, Naver, GitHub noreply, etc.)
Unified the main history’s author/committer to yyeongjin <appsky1888@gmail.com> and pushed the change to the remote
Saved the pre‑rewrite state in a local backup branch backup/before-author-email-rewrite-2026-06-17