feat(docs): comprehensive improvement (#3564)

* docs: comprehensive documentation improvements for humans and agents

New human docs:
- grpo.qmd: GRPO deep dive (async, rewards, IS correction, scaling)
- ebft.qmd: EBFT guide (structured/strided modes, feature extraction)
- choosing_method.qmd: decision tree for SFT vs LoRA vs DPO vs GRPO
- vllm_serving.qmd: vLLM setup for GRPO (server/colocate, LoRA sync)
- training_stability.qmd: monitoring, NaN debugging, OOM, healthy metrics

New agent docs:
- AGENTS_SFT.md: agent reference for supervised fine-tuning
- AGENTS_DPO.md: agent reference for preference learning (DPO/KTO/ORPO)

Updated existing docs:
- rlhf.qmd: cross-references to new GRPO/EBFT/choosing-method guides
- getting-started.qmd: reorganized Next Steps with links to new guides
- debugging.qmd: link to training stability guide
- _quarto.yml: added new pages to sidebar navigation

Removed:
- bak.agents.md: stale backup that confused agents

* docs: trim duplicated generic config from AGENTS_DPO.md

Remove boilerplate training params (optimizer, gradient_checkpointing,
flash_attention, etc.) from each method template. These are not
preference-learning-specific and are already covered in AGENTS_SFT.md.
Config templates now show only method-specific fields with a reference
to AGENTS_SFT.md for the rest.

* docs: deduplicate across new doc pages

- grpo.qmd: collapse vLLM setup section to brief config + link to
  vllm_serving.qmd; collapse IS correction to essentials + link;
  replace full monitoring tables with summary + link to
  training_stability.qmd
- vllm_serving.qmd: remove duplicated async/IS config reference tables
  (already in grpo.qmd config reference); replace full example config
  with link to grpo.qmd quick start
- ebft.qmd: trim generic training params in quick start config

* fix: train scripts

* feat: split files into cleaner parts

* fix: cleanup pretraining docs

---------

Co-authored-by: Wing Lian <wing.lian@gmail.com>

docs/getting-started.qmd

@@ -170,17 +170,26 @@ More details can be found in [Merging LoRA weights](inference.qmd#sec-merging).
 
 ## Next Steps {#sec-next-steps}
 
-Now that you have the basics, you might want to:
+Now that you have the basics, explore these guides based on what you want to do:
 
-- Try different model architectures
-- Experiment with hyperparameters
-- Use more advanced training methods
-- Scale up to larger models
+**Choose your path:**
 
-Check our other guides for details on these topics:
+- [Choosing a Fine-Tuning Method](choosing_method.qmd) — SFT vs LoRA vs QLoRA vs GRPO vs DPO, with hardware recommendations
 
-- [Configuration Guide](config-reference.qmd) - Full configuration options
-- [Dataset Loading](dataset_loading.qmd) - Loading datasets from various sources
-- [Dataset Formats](dataset-formats) - Working with different data formats
-- [Multi-GPU Training](multi-gpu.qmd)
-- [Multi-Node Training](multi-node.qmd)
+**Core guides:**
+
+- [Dataset Loading](dataset_loading.qmd) — Loading datasets from various sources
+- [Dataset Formats](dataset-formats) — Working with different data formats
+- [Optimizations](optimizations.qmd) — Flash attention, gradient checkpointing, sample packing
+- [Training Stability & Debugging](training_stability.qmd) — Monitoring metrics, fixing NaN, OOM debugging
+
+**Advanced training methods:**
+
+- [RLHF / Preference Learning](rlhf.qmd) — DPO, KTO, GRPO, EBFT
+- [GRPO Training](grpo.qmd) — RL with custom rewards and vLLM generation
+- [vLLM Serving](vllm_serving.qmd) — Setting up vLLM for GRPO
+
+**Scaling up:**
+
+- [Multi-GPU Training](multi-gpu.qmd) — DeepSpeed, FSDP, DDP
+- [Multi-Node Training](multi-node.qmd) — Distributed training across machines