From 2bc3912594c341a2570aa2c9a50ee655f117cb3a Mon Sep 17 00:00:00 2001
From: PENG Bo <33809201+BlinkDL@users.noreply.github.com>
Date: Wed, 13 Apr 2022 16:06:12 +0800
Subject: [PATCH] Update README.md

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 53343fa..aceca13 100644
--- a/README.md
+++ b/README.md
@@ -32,12 +32,12 @@ The pseudocode (execution from top to bottom):
 
 I propose a simple new method to find better LR schedules. The method is cost-efficient and practical for large LMs. The takeaway is we can model the loss curve dynamics (phenomenology) w.r.t. the LR, and a nice closed-form LR curve can be directly computed from it using variantional method. Moreover we can predict the final loss with reasonable accuracy.
 
-![better_lr_schedule](Research/better_lr_schedule.png)
-
-UPDATE: In "Conclusion 1.", remember to use the best-fitting regime (ignore the initial steps where our approximations break down) to fit the parameters.
+UPDATE: In "Conclusion 1.", remember to use the best-fitting regime (ignore the initial steps where our approximations break down) to fit the parameters. Try this: exponential LR decay from lr to 0.2 * lr in 3 hrs.
 
 In the last three plots, black = predicted loss curve of the new LR schedule, blue = original (unoptimized) real loss curve, orange = new LR schedule.
 
+![better_lr_schedule](Research/better_lr_schedule.png)
+
 # The top-p-x sampling method
 
 We propose a new sampling method called top-p-x: