MiniMind is an open-source project for training LLMs from scratch: a 64M Dense model and a 198M-A64M MoE model that run the entire chain — Pretrain → SFT → LoRA → DPO → PPO/GRPO/CISPO → Agentic RL — in ~2 hours on a single RTX 3090 at roughly 3 RMB (~$0.40). Every core algorithm is implemented natively in PyTorch with no high-level wrappers.
Switzerland's Apertus (8B/70B, 15T tokens, 1,000+ languages) filters opt-outs and personal data before training to satisfy the EU AI Act; Japan's LLM-jp consortium shipped LLM-jp-4 (12T tokens) in April 2026, claiming wins over GPT-4o and Qwen3-8B on standard benchmarks. Both prove that from-scratch training outside the English sphere is a data-governance problem, not a technical one — plus a note on RWKV-7 as the non-Transformer alternative.
"Open-source LLM" is a spectrum: weights-only (Llama), weights plus data (most fully open projects), or data order, intermediate checkpoints, and training logs all released (LLM360 K2, OLMo 3's model flow). This piece unpacks the two projects that pushed transparency furthest: OLMo 3 shipped the first fully open 32B thinking model in November 2025, and K2 is the first 65B-class model whose checkpoints even include optimizer states.
The hands-on installment of the series: rent an RTX 3090 on RunPod (Secure Cloud $0.5/hr, Community Cloud $0.22/hr), follow the MiniMind README through pretrain (~1.21h) + SFT (~1.10h), spend roughly $0.55–1.50 USD total, and chat with your own 64M model trained from scratch in the terminal.
Training from scratch only makes sense in three cases: you want to learn how training works, you have 10B+ clean tokens no open model has seen, or you need a fully transparent training process for research. Otherwise fine-tuning or RAG is almost always cheaper. This post collapses the series' main routes into one cost ladder and a decision tree.
YuLan-Mini is a 2.4B open-source model from Renmin University's AI Box lab, trained on 48 A800 GPUs with only 1.08T tokens — scoring 37.8 on MATH-500 and 64.0 on HumanEval, beating Qwen2/Qwen2.5 peer models trained on 7T–18T tokens at math and code. What's public isn't a slogan: per-phase data mixes, pre-annealing optimizer states, and even W&B logs of the ablation studies.
Traditional document parsing runs a fixed pipeline regardless of input, but contracts, financial reports, and technical manuals each need different strategies. Agentic Parsing lets LLM agents observe a document and dynamically choose tools — AgenticOCR parses only the regions that matter (70%+ visual token savings), and ParseBench shows even the best method scores only 84.9% across 2,000 enterprise pages. No silver bullet.
ColPali renders each PDF page as an image, generates patch-level multi-vector embeddings with a vision-language model, and retrieves via MaxSim late interaction. On table-heavy financial PDFs, recall jumps from 62% to 84% — no OCR, no chunking. The tradeoff: ~100× storage, GPU required, no BM25.
Small chunks give precise embeddings but lack context; big chunks have complete context but diluted embeddings. Hierarchical Chunking builds multi-level indexes (2048→512→128 tokens) with an Auto-Merge algorithm: leaf nodes match precisely, and when hit density exceeds a threshold the parent node is returned to the LLM instead. HiChunk shows a 12.7% evidence recall improvement; LlamaIndex and Haystack have it built in.
AI boosted task output by 34%, but code review time surged 441% and measured delivery actually slowed 19%. A four-round research survey maps the current landscape: deterministic guardrails (hooks) vs probabilistic ones (prompts), clean-context review, self-improving feedback loops, specification-driven development, AI test quality crisis (high coverage but median 53% mutation score), and the Replit agent fabricating test results.
Standard RAG retrieves one set of documents per query, but real questions often need reasoning across 2-4 documents. IRCoT pioneered interleaved retrieval-reasoning, PAR²-RAG beats IRCoT by 23.5% accuracy on four benchmarks, and CompactRAG compresses LLM calls down to just two.
Self-RAG trains four reflection tokens (Retrieve / IsREL / IsSUP / IsUSE) into an LLM, letting it decide on-the-fly whether to retrieve, whether results are relevant, and whether its own output is grounded. ICLR 2024 Oral (top 1%), the 7B model beats ChatGPT and Llama2-chat + RAG on multiple QA benchmarks. The catch: it requires fine-tuning—no API-only models.
Markdown-KV format achieves 60.7% LLM comprehension accuracy vs 44.3% for CSV — a 16-point gap from format alone. But retrieval and comprehension have different optimal formats: metadata prepend + row-wise key-value is the current best combination for table RAG.
looplane now has a Cloudflare Durable Object run resource with async creation, status/cancel/artifacts, live NDJSON, and Last-Event-ID SSE; remote approvals use a separate short-lived capability. Python also provides an attach client and a stateful conversation WebSocket. Production deployment, cross-runtime parity, and full multi-tenant hardening remain unverified.
Looplane now grades effects as read/modify/modify_execute/execute and fails closed on unclassified tools. Native MCP tools default to execute unless trusted read-only metadata lowers them. Approval events still land in events.jsonl first and grants can scope to one change set or backend; general command rules and universal sandbox coupling remain unfinished.
looplane now ships a bounded tool-program DSL: read-only programs support list/read/search/diff, repeat, and if_contains; modify/check transactions receive whole-transaction approval and roll back touched paths on failure. This is not arbitrary JavaScript/Python code mode, and transaction execution is not parallel.
Mature-agent compaction must handle triggers, complete-turn cut points, and recovery. looplane now has an 85% high-watermark, automatic compaction, a deterministic native-loop fallback summary, persisted checkpoints, and workspace-context reinjection. Cross-runtime fallback, model-quality summaries, and live-provider long-session validation remain open.
omp and claude-code provide cross-session memory while the other references mostly rely on instruction files. looplane now has an explicit remember/list/inject baseline: typed JSONL memories enter prompts across sessions, but retrieval is scope-and-recency only, with no semantic ranking, deduplication, forget command, or automatic extraction.
All five projects combine allow/ask/deny decisions, compound-command inspection, and fail-closed behavior. looplane now has a deny-first classifier, critical floor, shell segmentation, timeout-deny, configured allow/deny rules, and visible policy reasons. Broader syntax coverage and live interactive validation remain open.
I'm building my own Python coding agent called looplane. This series dissects the source code of five mature projects — pi, oh-my-pi, opencode, codex, and claude-code — topic by topic, while also comparing them with Looplane's current TUI, external CLI runtimes, local gateway, usage/OTel/session tooling, and Cloudflare slice. Every post follows a fixed five-part structure: design problem → how five projects do it → looplane's choice → academic grounding → improvement roadmap, with evidence cited at file#symbol level.