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+# MaxText trainers
+
+This package holds MaxText's training entry points, grouped by stage
+(`pre_train`, `post_train`, etc.). Each trainer is a runnable module launched
+with `python -m maxtext.trainers.<...>`. Most trainers take a config YAML plus
+`key=value` overrides.
+
+This README is a map of the package. For concepts and step-by-step guides, see
+the [documentation website](https://maxtext.readthedocs.io/en/latest/).
+
+## Layout
+
+| Package | Purpose |
+|---|---|
+| `pre_train` | Core pretraining/finetuning loop (`train.py`) and ahead-of-time compilation (`train_compile.py`) |
+| `diloco` | DiLoCo low-communication training strategy: a library layered onto the pretraining loop |
+| `post_train` | Post-training regimes: SFT, DPO, RL (GRPO/GSPO), and knowledge distillation |
+| `tokenizer` | Train a SentencePiece tokenizer from a Grain dataset |
+
+## Entry points
+
+Most runnable modules (except for `tokenizer`; more details below) have a
+default config from [`configs/pyconfig.py`](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/configs/pyconfig.py) that can be
+overridden on the command line. Modules with no entry in that mapping fall back
+to `base.yml` with a warning, so pass a config path explicitly when `base.yml`
+is not the right config.
+
+For example, to run the pretraining loop with a custom run name:
+
+```bash
+python -m maxtext.trainers.pre_train.train run_name=<run_name>
+```
+
+Pass a YAML as the first positional argument to override the default:
+
+```bash
+python -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=<run_name>
+```
+
+### Pretraining — `pre_train`
+
+Train a model from scratch or continue training an existing checkpoint on raw
+text with a next-token prediction loss. `train.py` is MaxText's native training
+loop and supports both Flax Linen (`nn.Module`) and Flax NNX models;
+`train_compile.py` does not train, but compiles that loop's train step for a
+target topology.
+
+- `pre_train.train`: uses `base.yml` as default config file. See
+  [pretraining.md](https://maxtext.readthedocs.io/en/latest/tutorials/pretraining.html) for a step-by-step
+  guide.
+- `pre_train.train_compile`: uses `base.yml` as default config file. Performs
+  ahead-of-time (XAOT) compilation. See
+  [Features and diagnostics](https://maxtext.readthedocs.io/en/latest/guides/monitoring_and_debugging/features_and_diagnostics.html)
+  for more details.
+
+### DiLoCo — `diloco`
+
+DiLoCo is a low-communication training strategy layered onto the pretraining
+loop. It is gated by `enable_diloco` and the `diloco_*` flags in `base.yml`, and
+is consumed via [`utils/train_utils.py`](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/utils/train_utils.py). DiLoCo
+requires NNX models and has no entry point of its own; set `enable_diloco=True`
+on a pretraining run to use it. For example,
+
+```bash
+python -m maxtext.trainers.pre_train.train \
+  enable_diloco=true \
+  dcn_diloco_parallelism=2 \
+  run_name=<run_name>
+```
+
+### Post-training — `post_train`
+
+Adapt an already-pretrained checkpoint: follow instructions (SFT), align to
+preference pairs (DPO), optimize against a reward signal (RL), or train a
+student model against a teacher's outputs (distillation). Most of these wrap
+MaxText models in [Tunix](https://github.com/google/tunix) trainers rather than
+reusing the native loop: `MaxTextPeftTrainer` (SFT) and
+`MaxTextDistillationTrainer` (distillation) subclass Tunix's `PeftTrainer`, and
+DPO uses Tunix's `DPOTrainer` directly.
+
+| Regime | Module | Default config | Docs |
+|---|---|---|---|
+| SFT | `post_train.sft.train_sft` | `post_train/sft.yml` | [sft.md](https://maxtext.readthedocs.io/en/latest/tutorials/posttraining/sft.html) |
+| SFT (native loop) | `post_train.sft.train_sft_native` | `post_train/sft.yml` | [sft.md](https://maxtext.readthedocs.io/en/latest/tutorials/posttraining/sft.html) |
+| DPO | `post_train.dpo.train_dpo` | `post_train/dpo.yml` | [dpo.md](https://maxtext.readthedocs.io/en/latest/tutorials/posttraining/dpo.html) |
+| RL (GRPO, GSPO) | `post_train.rl.train_rl` | `post_train/rl.yml` | [rl.md](https://maxtext.readthedocs.io/en/latest/tutorials/posttraining/rl.html) |
+| Distillation | `post_train.distillation.train_distill` | `post_train/distillation.yml` | [distillation README](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/trainers/post_train/distillation/README.md), [knowledge_distillation.md](https://maxtext.readthedocs.io/en/latest/tutorials/posttraining/knowledge_distillation.html) |
+
+**Native-loop SFT** — `train_sft_native` reuses `pre_train/train.py`, appending
+`use_sft=True` and `use_tunix_gradient_accumulation=False` to the config
+overrides.
+
+**RL variants** — a single `train_rl` covers GRPO (the default) and GSPO
+(`loss_algo=gspo-token`). A separate legacy implementation lives outside this
+package at [`experimental/rl`](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/experimental/rl/README.md); it takes two
+config files (train + inference) and has no default-config mapping. New work
+should use `post_train.rl.train_rl`.
+
+**Hooks** — `post_train/hooks.py` defines `BaseTrainingHooks` and
+`BaseDataHooks`; SFT and DPO subclass both (`sft/hooks.py`, `dpo/hooks.py`). RL
+is the exception: `rl/hooks.py` extends Tunix's `TrainingHooks` directly to fire
+`evaluate(...)` every `eval_interval` outer steps, and defines no data hooks.
+
+**Distillation** — `train_distill` loads dual student/teacher models, with an
+optional Learn-to-Init phase gated by `learn_to_init_mode` (declared in
+[`configs/types.py`](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/configs/types.py) rather than in any shipped YAML). The
+offline top-k logit pipeline below is runnable but not a trainer, and has no
+entry in the config mapping, so pass a config explicitly:
+
+| Module | Docs |
+|---|---|
+| `post_train.distillation.save_top_k_teacher_logits` | [distillation README](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/trainers/post_train/distillation/README.md) |
+| `post_train.distillation.verify_saved_logits` | [distillation README](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/trainers/post_train/distillation/README.md) |
+
+### Tokenizer — `tokenizer`
+
+Fit a SentencePiece vocabulary over a Grain corpus (`parquet`, `arrayrecord`,
+or `tfrecord`) and write the tokenizer model to the assets directory, ready to
+feed a pretraining run. Configured entirely with absl flags, rather than a
+config YAML (`--grain_train_files` is required).
+
+Example usage:
+
+```bash
+python -m maxtext.trainers.tokenizer.train_tokenizer \
+  --grain_train_files=gs://my-bucket/data/*.parquet \
+  --grain_file_type=parquet
+```
+
+## Related docs
+
+- [Model configs](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/configs/README.md) — tuned per-hardware model configs and XLA flags
+- [Pretraining](https://maxtext.readthedocs.io/en/latest/tutorials/pretraining.html)
+- [Post-training index](https://maxtext.readthedocs.io/en/latest/tutorials/post_training_index.html)
+- [Distillation guide](https://maxtext.readthedocs.io/en/latest/guides/distillation.html)