docs: add serial commas throughout the README (#2593)

README.md

@@ -34,9 +34,9 @@
 Miles is a high-performance, enterprise-ready reinforcement learning framework for
 **large-scale model post-training**. It pairs [SGLang](https://github.com/sgl-project/sglang)
 for high-throughput rollout with [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) for
-scalable training, and ships the precision, stability and observability features an RL run
+scalable training, and ships the precision, stability, and observability features an RL run
 needs at trillion-parameter scale. A PyTorch FSDP2 backend is available for runs that would
-rather train the HuggingFace implementation as-is, though the recipes, the parallelism and
+rather train the HuggingFace implementation as-is, though the recipes, the parallelism, and
 the largest models all live on Megatron-LM. See
 [Training Backends](https://miles.radixark.com/docs/user-guide/training-backend).
 
@@ -48,7 +48,7 @@ the largest models all live on Megatron-LM. See
   off-policy schedules, a pipeline tuned for fewer bubbles, and customizable async rollout
   and eval modes. See [Fully Async RL](https://miles.radixark.com/docs/user-guide/fully-async).
 - **Fast agentic rollout.** Generation runs on [SGLang](https://github.com/sgl-project/sglang)
-  behind a router that spreads requests across engines, preserves per-request metadata and
+  behind a router that spreads requests across engines, preserves per-request metadata, and
   health-checks the fleet. Tuned for multi-turn agentic workloads.
 - **Fast weight updates.** New weights reach the engines in-loop in seconds, even on a
   trillion-parameter model such as Kimi-K2.6, with
@@ -56,7 +56,7 @@ the largest models all live on Megatron-LM. See
   for disaggregated setups.
 - **Low-precision training.** [MXFP8 and NVFP4](https://miles.radixark.com/docs/advanced/low-precision)
   training with a numerically stable RL recipe that reduces precision-induced divergence.
-  FP8, [INT4 QAT](https://miles.radixark.com/docs/advanced/int4-qat), BF16 and FP16 are also
+  FP8, [INT4 QAT](https://miles.radixark.com/docs/advanced/int4-qat), BF16, and FP16 are also
   supported.
 - **LoRA and multi-LoRA.** [Low-rank adapters](https://miles.radixark.com/docs/advanced/lora)
   train frontier-scale models on a fraction of the GPUs, and the same adapters load straight
@@ -66,7 +66,7 @@ the largest models all live on Megatron-LM. See
 
 - **Token-in-token-out (TITO).** Supported for
   [every model and every black-box harness](https://miles.radixark.com/docs/user-guide/agentic-rollout),
-  with no detokenize and retokenize round-trip between rollout and training.
+  with no detokenize/retokenize round-trip between rollout and training.
 - **Rollout Routing Replay (R3).** Expert routing recorded during rollout is
   [replayed in the trainer's forward pass](https://miles.radixark.com/docs/advanced/miles-router),
   removing the MoE routing mismatch that destabilizes large runs, with compute and
@@ -77,18 +77,18 @@ the largest models all live on Megatron-LM. See
 
 ### What Miles runs
 
-- **Day-0 model support.** DeepSeek-V4, Kimi-K3, GLM-5.2, Inkling and Nemotron landed on
+- **Day-0 model support.** DeepSeek-V4, Kimi-K3, GLM-5.2, Inkling, and Nemotron landed on
   release day. Beyond day 0, nearly every frontier model runs on Miles, including Kimi-K2.6
   and Qwen3.5. See [Models](https://miles.radixark.com/docs/models).
-- **Extensive hardware support.** NVIDIA GB300, GB200, B300, B200, H200, H100 and A100, and
-  AMD MI300X, MI325, MI350 and MI355X via ROCm. See
+- **Extensive hardware support.** NVIDIA GB300, GB200, B300, B200, H200, H100, and A100, and
+  AMD MI300X, MI325, MI350, and MI355X via ROCm. See
   [Installation](https://miles.radixark.com/docs/getting-started/installation#hardware-requirements)
   for per-GPU status and the container image for each.
-- **Wide recipe support.** GRPO, GSPO, PPO and REINFORCE++ for RL, plus SFT and
+- **Wide recipe support.** GRPO, GSPO, PPO, and REINFORCE++ for RL, plus SFT and
   [on-policy distillation](https://miles.radixark.com/docs/advanced/on-policy-distillation).
 - **Agentic environments.** Train coding and computer-use agents through connectors for
-  Harbor, HUD, NeMo Gym, OpenEnv, Verifiers and more, each plugging into the rollout
-  layer that fits it, with task sandboxes on AgentENV, Daytona, E2B or Modal. See
+  Harbor, HUD, NeMo Gym, OpenEnv, Verifiers, and more, each plugging into the rollout
+  layer that fits it, with task sandboxes on AgentENV, Daytona, E2B, or Modal. See
   [Agentic Environments](https://miles.radixark.com/docs/user-guide/environments).
 - **Diffusion models.** Flow-GRPO, DiffusionNFT and SFT on an sglang-diffusion rollout
   engine and an FSDP2 trainer, in
@@ -108,7 +108,7 @@ the largest models all live on Megatron-LM. See
 
 Miles was forked from [slime](https://github.com/THUDM/slime), and integrates
 [SGLang](https://github.com/sgl-project/sglang),
-[Megatron-LM](https://github.com/NVIDIA/Megatron-LM) and
+[Megatron-LM](https://github.com/NVIDIA/Megatron-LM), and
 [torch_memory_saver](https://github.com/fzyzcjy/torch_memory_saver).
 
 Miles is shaped by the teams that build on it and support its development,