docs: update homepage Core features section per the v0.1 feature list

docs/advanced/index.md

@@ -3,8 +3,9 @@ title: Advanced Features
 description: Systems-level features for large-scale and long-running RL.
 ---
 This section covers the Miles features that the Core-features section of the
-homepage points at: low-precision training (FP8 / MXFP8 / INT4 QAT), Rollout
-Routing Replay for MoE, speculative decoding, and LoRA training and serving.
+homepage points at: low-precision training (FP8 / MXFP8 / NVFP4 / INT4 QAT),
+Rollout Routing Replay for MoE, fast weight updates over P2P RDMA, fault
+tolerance, speculative decoding, and LoRA training and serving.
 
 <CardGroup cols={2}>
 

docs/index.md

@@ -11,30 +11,61 @@ needed to run RL at trillion-parameter scale.
 
 ## Core features
 
-- **Fast and stable support for the latest models.** Day-0 enablement of frontier
-  releases such as DeepSeek-V4, with rapid follow-on support for new architectures
-  including GLM-5, Qwen 3.6, and Nemotron-3-Super.
-- **Unified low-precision training.** Customizable precision across the rollout and
-  training engines, with unified **BF16**, **FP8**, **MXFP8**, and **INT4 QAT** recipes
-  available now and an **NVFP4** training recipe in progress.
-- **Efficient Rollout Routing Replay (R3).** For MoE models, expert routing captured
-  during inference is replayed during the trainer's forward pass, eliminating the
-  mismatch that destabilizes large-scale MoE RL. Optimized with a routing-result cache
-  and overlapped device-to-host (D2H) copy to reduce overhead in both single-turn and
-  multi-turn RL.
-- **Speculative rollout with online MTP-SFT.** Miles keeps the draft model's acceptance
-  rate high through training by fine-tuning MTP layers on-policy.
-- **LoRA training and serving.** Both SFT and RL recipes support LoRA adapters,
-  and the same adapters load directly into SGLang for rollout — no separate
-  merge or conversion step.
-- **Native agentic rollout.** Tool use, multi-turn dialogue, search, code
-  execution, and multi-agent co-evolution are all supported through clean Python
-  extension points.
-- **Minimal core, maximal extension.** Twenty-plus plug-points let you replace the
-  rollout, reward, loss, or filter without forking the trainer.
-- **Broad hardware support.** First-class on NVIDIA Hopper (H100, H200) and
-  Blackwell (B100, B200, GB200, GB300), with AMD MI300X / MI325 / MI350 /
-  MI355X also supported via ROCm.
+### Efficiency & stability
+
+- **Fully async RL.** Rollout and training workers are decoupled, with configurable
+  on- and off-policy schedules, an optimized pipeline with fewer bubbles, and
+  customizable async rollout and eval modes. See
+  [Fully Async Rollout](/user-guide/fully-async).
+- **Fast agentic rollout.** High-throughput generation on
+  [SGLang](https://github.com/sgl-project/sglang), optimized for multi-turn
+  agentic workloads.
+- **Fast weight updates.** Updated weights sync back in-loop without pausing
+  rollout — under 10 seconds for a model with 1 T parameters — with
+  [P2P RDMA](/advanced/p2p-weight-transfer) as a fast path for disaggregated setups.
+- **Unified low-precision training.** [MXFP8 and NVFP4](/advanced/fp8-low-precision)
+  training with a numerically stable RL recipe that reduces precision-induced
+  divergence; FP8, [INT4 QAT](/advanced/int4-qat), BF16, and FP16 are also supported.
+- **Token-in-token-out (TITO).** Supported for
+  [all models and all black-box agent harnesses](/user-guide/agentic-chat-template) —
+  no detokenize/retokenize round-trips between rollout and training.
+- **Rollout Routing Replay (R3).** Expert routing recorded during rollout is
+  [replayed in the trainer's forward pass](/advanced/miles-router), eliminating the
+  routing mismatch that destabilizes large-scale MoE RL, with compute and
+  communication overlapped to minimize overhead.
+- **LoRA and multi-LoRA.** [Low-rank adapters](/advanced/lora) train frontier-scale
+  models on a fraction of the GPUs, and the same adapters load directly into SGLang
+  for rollout — no separate merge or conversion step.
+- **Fault tolerance.** When an SGLang engine dies, Miles
+  [recovers it and resumes the run in place](/advanced/fault-tolerance) — no
+  restart, no pause.
+- **Day-0 model support.** Day-0 enablement of frontier releases such as
+  DeepSeek-V4, Kimi-K3, GLM-5.2, Inkling, and Nemotron — and beyond day-0, nearly
+  all frontier models (see [Supported models](#supported-models)).
+
+### Design, support & user experience
+
+- **Coding-agent sandboxes and examples.** [Harbor](/user-guide/harbor),
+  [OpenEnv](/user-guide/openenv), and [NeMo-Gym](/user-guide/nemo-gym) integrations,
+  running local CPU sandboxes or per-episode sandboxes on
+  [Daytona](https://www.daytona.io/), [E2B](https://e2b.dev/), and self-hosted
+  [AgentENV](https://github.com/kvcache-ai/AgentENV) — see
+  [Environments](/user-guide/environments) for the support matrix.
+- **Highly customizable pipeline.** Shape every workload through
+  [twenty-plus plug-points](/user-guide/customization), from reward computation to
+  the full rollout function.
+- **Megatron or FSDP.**
+  [Switch training backends](/developer/experimental-features#fsdp-backend) without
+  rewriting your training loop.
+- **Wide recipe support.** RL (GRPO, PPO), SFT, and on-policy distillation.
+- **Verified on multiple hardware generations.** GB300, GB200, B300, B200, H200,
+  H100, and AMD MI355X / MI300X — see [Platforms](/platforms/index).
+- **Comprehensive CI.** Unit suites run on every pull request, and tag-triggered
+  end-to-end GPU training tests cover the supported model families on both NVIDIA
+  and AMD runners.
+- **[Miles dashboard](/user-guide/dashboard).** A self-hosted web UI for a run's
+  training dynamics and compute efficiency: what every GPU was doing during a step,
+  and what each trajectory contained at the token level.
 
 ## Supported models