diff --git a/README.md b/README.md
index 993b10b..19af817 100644
--- a/README.md
+++ b/README.md
@@ -37,6 +37,7 @@
   - [Choosing an Integration](#choosing-an-integration)
   - [Examples](#examples)
   - [Inference Benchmarks](#inference-benchmarks)
+  - [Finetune](#finetune)
   - [Limitations](#limitations)
 - [Ecosystem](#ecosystem)
 - [News](#news)
@@ -641,6 +642,12 @@ Cosmos 3 latency and serving numbers live in [`inference_benchmarks.md`](inferen
 | [Cosmos3-Super generator](inference_benchmarks.md#cosmos3-super-generator) | Generator | Cosmos3-Super | The same modalities and engines at the larger checkpoint scale |
 | [Cosmos3-Nano reasoner](inference_benchmarks.md#cosmos3-nano-reasoner) | Reasoner | Cosmos3-Nano | vLLM serving metrics — TTFT, request latency, and throughput at concurrency 1/64/128/256 |
 
+### Finetune
+
+Finetune Cosmos 3 with the [Cosmos Framework](https://github.com/NVIDIA/cosmos-framework), NVIDIA's end-to-end Physical AI framework for training and serving world models. It provides runnable setup, inference, omni-model training, and evaluation workflows for the Generator and Reasoner surfaces, with reference recipes for vision, action, and reasoning post-training.
+
+See the [Cosmos Framework training guide](https://github.com/NVIDIA/cosmos-framework/blob/main/docs/training.md) for the full post-training workflow, including data preparation, configuration, and launch commands.
+
 ### Limitations
 
 Cosmos 3 can produce artifacts in long, high-resolution, or physically complex outputs. Common failure modes include temporal inconsistency, unstable camera or object motion, inaccurate sound-video alignment, imperfect action-state consistency, object morphing, inaccurate 3D structure, and implausible physical dynamics. Applications that require physically grounded simulation, safety-critical control, or complex multi-agent behavior need additional validation, guardrails, and system-level safety analysis before deployment.