diff --git a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md
index 8bcf0dc0..85f72d87 100644
--- a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md	
+++ b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md	
@@ -29,10 +29,13 @@ Something to justify, why diffusion model as opposed to other generative AI
 6. Diffusion model uses two processes
 7. a forward process that introduces noise into an 'input'
 8. this forward process does this using several small steps of noise
-9. This noise is a gaussian noise
-10. over time this forward process degrades the input until it is unrecognizable
-11. This takes several several iterations, but is dependent on the amount of noise in each step
-12. The markov chain that this creates is also gaussian at every step, until the noise at the end of the day is some gaussian distribution. Usually mean 0 std. dev beta
-13. A reverse process that tries to remove the noise
-14. But if we destroy the input how can we do this?
-15. Well we train a neural network as a denoiser.
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+9. These are called timesteps
+10. This noise is a gaussian noise
+11. over time this forward process degrades the input until it is unrecognizable
+12. This takes several several iterations, but is dependent on the amount of noise in each step
+13. The markov chain that this creates is also gaussian at every step, until the noise at the end of the day is some gaussian distribution. Usually mean 0 std. dev beta
+14. A reverse process that tries to remove the noise
+15. But if we destroy the input how can we do this?
+16. Well we train a neural network as a denoiser.
+17. Because the diffusion model forward steps are small and gaussian, we can know the reverse step is also a gaussian distribution. 
+18. So for our neural network, what we're trying to learn is the mean and standard deviation of the reverse steps for a given timestep. 
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