diff --git a/src/inference.rs b/src/inference.rs
index 96b68b0b..40a93d1c 100644
--- a/src/inference.rs
+++ b/src/inference.rs
@@ -17,7 +17,7 @@ use burn::tensor::ElementConversion;
 pub type Weights = [f32];
 
 pub static DEFAULT_WEIGHTS: [f32; 17] = [
-    0.4, 0.6, 2.4, 5.8, 4.93, 0.94, 0.86, 0.01, 1.49, 0.14, 0.94, 2.18, 0.05, 0.34, 1.26, 0.29,
+    0.4, 0.9, 2.3, 10.9, 4.93, 0.94, 0.86, 0.01, 1.49, 0.14, 0.94, 2.18, 0.05, 0.34, 1.26, 0.29,
     2.61,
 ];
 
@@ -387,7 +387,7 @@ mod tests {
         let metrics = fsrs.evaluate(items.clone(), |_| true).unwrap();
 
         Data::from([metrics.log_loss, metrics.rmse_bins])
-            .assert_approx_eq(&Data::from([0.20944944, 0.042762663]), 5);
+            .assert_approx_eq(&Data::from([0.20753297, 0.041122540]), 5);
 
         let fsrs = FSRS::new(Some(WEIGHTS))?;
         let metrics = fsrs.evaluate(items, |_| true).unwrap();
diff --git a/src/model.rs b/src/model.rs
index 2d0b1fc5..f94eafa1 100644
--- a/src/model.rs
+++ b/src/model.rs
@@ -282,7 +282,7 @@ mod tests {
         let stability = model.init_stability(rating);
         assert_eq!(
             stability.to_data(),
-            Data::from([0.4, 0.6, 2.4, 5.8, 0.4, 0.6])
+            Data::from([0.4, 0.9, 2.3, 10.9, 0.4, 0.9])
         )
     }
 
diff --git a/src/optimal_retention.rs b/src/optimal_retention.rs
index abbc2e24..59e4dea6 100644
--- a/src/optimal_retention.rs
+++ b/src/optimal_retention.rs
@@ -436,7 +436,7 @@ mod tests {
             0.9,
             None,
         );
-        assert_eq!(memorization, 2542.50223082592)
+        assert_eq!(memorization, 2635.689850107157)
     }
 
     #[test]
@@ -444,7 +444,7 @@ mod tests {
         let config = SimulatorConfig::default();
         let fsrs = FSRS::new(None)?;
         let optimal_retention = fsrs.optimal_retention(&config, &[], |_v| true).unwrap();
-        assert_eq!(optimal_retention, 0.8687319006249048);
+        assert_eq!(optimal_retention, 0.8633668648071942);
         assert!(fsrs.optimal_retention(&config, &[1.], |_v| true).is_err());
         Ok(())
     }
diff --git a/src/pre_training.rs b/src/pre_training.rs
index 4007dbec..fea6e72b 100644
--- a/src/pre_training.rs
+++ b/src/pre_training.rs
@@ -4,7 +4,7 @@ use itertools::Itertools;
 use ndarray::Array1;
 use std::collections::HashMap;
 
-static R_S0_DEFAULT_ARRAY: &[(u32, f32); 4] = &[(1, 0.4), (2, 0.6), (3, 2.4), (4, 5.8)];
+static R_S0_DEFAULT_ARRAY: &[(u32, f32); 4] = &[(1, 0.4), (2, 0.9), (3, 2.3), (4, 10.9)];
 
 pub fn pretrain(fsrs_items: Vec<FSRSItem>, average_recall: f32) -> Result<[f32; 4]> {
     let pretrainset = create_pretrain_data(fsrs_items);
@@ -340,7 +340,7 @@ mod tests {
             ],
         )]);
         let actual = search_parameters(pretrainset, 0.9);
-        let expected = [(4, 1.2502396)].into_iter().collect();
+        let expected = [(4, 1.4098487)].into_iter().collect();
         assert_eq!(actual, expected);
     }
 
@@ -352,15 +352,15 @@ mod tests {
         let pretrainset = split_data(items, 1).0;
         assert_eq!(
             pretrain(pretrainset, average_recall).unwrap(),
-            [0.948_268_3, 1.696_434_9, 4.059_253_7, 9.001_528,],
+            [0.948_268_3, 1.695_154, 4.051_595_7, 9.332_188,],
         )
     }
 
     #[test]
     fn test_smooth_and_fill() {
-        let mut rating_stability = HashMap::from([(1, 0.4), (3, 2.4), (4, 5.8)]);
+        let mut rating_stability = HashMap::from([(1, 0.4), (3, 2.3), (4, 10.9)]);
         let rating_count = HashMap::from([(1, 1), (2, 1), (3, 1), (4, 1)]);
         let actual = smooth_and_fill(&mut rating_stability, &rating_count).unwrap();
-        assert_eq!(actual, [0.4, 0.81906897, 2.4, 5.8,]);
+        assert_eq!(actual, [0.4, 0.8052433, 2.3, 10.9,]);
     }
 }