diff --git a/data/run_lengths/run_lengths_128.csv b/data/run_lengths/run_lengths_128.csv
new file mode 100644
index 0000000..9564606
--- /dev/null
+++ b/data/run_lengths/run_lengths_128.csv
@@ -0,0 +1,1546 @@
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diff --git a/data/run_lengths/run_lengths_2.csv b/data/run_lengths/run_lengths_2.csv
new file mode 100644
index 0000000..9564606
--- /dev/null
+++ b/data/run_lengths/run_lengths_2.csv
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+2,ehrlich_holo_large,coordinate_line_bo,3,9232a77e-8c75-4678-890e-f71d7973a4fa,1019
+3,ehrlich_holo_large,coordinate_line_bo,4,158c12a0-03f3-4082-95d3-ab8076be6aa0,1018
+4,ehrlich_holo_large,coordinate_line_bo,5,ad15b3e2-5621-4e2e-855b-2bfe73c4b69d,1023
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+1,ehrlich_holo_large,vanilla_bo_hvarfner,2,3f451fbd-82bb-4236-bc9e-5c6535b18967,1000
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+3,ehrlich_holo_large,vanilla_bo_hvarfner,4,ca765df1-7951-4cec-b170-444cd6674a0c,1001
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+2,ehrlich_holo_large,pr,3,7a16ae19-a9c1-4e44-9f26-78ad9f81c570,996
+3,ehrlich_holo_large,pr,4,3774faf0-5e60-4fbc-b6b4-e63a7d9d9b36,999
+4,ehrlich_holo_large,pr,5,e81de5b7-79a0-474a-8ee2-5812d37dac29,996
+0,ehrlich_holo_large,saas_bo,1,c0a891ff-eb11-47a3-80bb-c07f9e997fdc,1000
+1,ehrlich_holo_large,saas_bo,2,57dbff08-8f88-4a55-8dfb-ebd3079734e7,999
+2,ehrlich_holo_large,saas_bo,3,adf58767-b8f0-4e2a-82f0-467ef170e9de,998
+3,ehrlich_holo_large,saas_bo,4,b0bdf67c-7cea-4b32-a173-fc155c7d54b6,1000
+4,ehrlich_holo_large,saas_bo,5,ca2aaffe-9de6-4169-951a-76a7a72bf987,1000
+0,pest_control_equivalent,directed_evolution,1,5960ba93-99dc-465c-a5c0-fd7ae56efc21,309
+1,pest_control_equivalent,directed_evolution,2,9bbe61a3-73df-4f17-9437-34fd3d5ad8e4,309
+2,pest_control_equivalent,directed_evolution,3,5b6fca94-6230-4cba-8f6c-33c6526eeacb,309
+3,pest_control_equivalent,directed_evolution,4,ceffe36b-240e-4512-89ca-d081d2a47b7e,309
+4,pest_control_equivalent,directed_evolution,5,97d0abb3-5e35-4db4-9a82-6d0a9dedfd17,309
+0,pest_control_equivalent,hill_climbing,1,af568b27-dac9-47fb-8757-7dfe7637862e,309
+1,pest_control_equivalent,hill_climbing,2,d15d7a9a-ccc0-40e7-8bcb-437f87f8fe73,309
+2,pest_control_equivalent,hill_climbing,3,2ee4de42-ff70-4650-9a8b-2ac1e8b3f983,309
+3,pest_control_equivalent,hill_climbing,4,dd31c1ca-0bea-4879-a1b3-e4adad593fae,309
+4,pest_control_equivalent,hill_climbing,5,746ef089-103e-481e-8831-959bd0d9c9b0,309
+0,pest_control_equivalent,genetic_algorithm,1,8f38c8ef-8396-4557-a13f-e23b3ac7cc61,309
+1,pest_control_equivalent,genetic_algorithm,2,112d0220-d14b-4ad0-8dc0-89dee09ca93b,309
+2,pest_control_equivalent,genetic_algorithm,3,1378e143-378c-466d-8465-3c3687a7aceb,309
+3,pest_control_equivalent,genetic_algorithm,4,ac91eaca-47c0-4784-91a0-76078e95be2a,309
+4,pest_control_equivalent,genetic_algorithm,5,5b65e910-efc7-4ad3-8bc6-aaf7807962eb,309
+0,pest_control_equivalent,cma_es,1,bd87f69b-2bbc-4205-80bf-ec99e6132266,309
+1,pest_control_equivalent,cma_es,2,3e38badd-093b-4737-9946-f6763af5ca37,309
+2,pest_control_equivalent,cma_es,3,b6e95210-baff-4013-82af-d2d5e3fbdada,309
+3,pest_control_equivalent,cma_es,4,3b7cc4f5-3ca1-48fd-9d65-5d673fe79db0,309
+4,pest_control_equivalent,cma_es,5,9f1acf24-c9be-44e5-9511-a6593c1169b4,309
+0,pest_control_equivalent,random_line_bo,1,aa4ca9b4-e963-4e3d-8cb6-28d9b2fcce43,309
+1,pest_control_equivalent,random_line_bo,2,02d1f72b-492a-4e1a-9f32-845c01f58cfa,309
+2,pest_control_equivalent,random_line_bo,3,5e14e275-0b2a-4918-b8f1-ac155deb13ca,309
+3,pest_control_equivalent,random_line_bo,4,a146a393-bd6e-4cdf-bc4a-5dafe1505a4d,309
+4,pest_control_equivalent,random_line_bo,5,688f8dfb-7a1a-42c3-a532-46a000809696,309
+0,pest_control_equivalent,coordinate_line_bo,1,9c3478d0-7346-471e-996e-d676fcbe367b,309
+1,pest_control_equivalent,coordinate_line_bo,2,06a5e576-94cb-4bb4-849b-3a794cacfb6a,309
+2,pest_control_equivalent,coordinate_line_bo,3,612bbceb-b329-48d9-9657-dcf61228e817,309
+3,pest_control_equivalent,coordinate_line_bo,4,e8ec7e4e-aa6d-4f43-9579-80f6e1282ab6,309
+4,pest_control_equivalent,coordinate_line_bo,5,3bfaf0cc-7649-4c25-80e5-f9d39774ec74,309
+0,pest_control_equivalent,turbo,1,8017061a-d971-4f55-be3a-7b2b792190f2,12
+1,pest_control_equivalent,turbo,2,fb64194d-6f3e-45ba-bd74-9f1cfec66c3d,309
+2,pest_control_equivalent,turbo,3,9448c890-dc71-4f75-b566-6d30d718bb0d,309
+3,pest_control_equivalent,turbo,4,8a8e1f06-5b2c-466a-88fc-fa814b635e3f,309
+4,pest_control_equivalent,turbo,5,1b5c31fc-b5fa-45fc-804b-f48a9db4afbb,309
+0,pest_control_equivalent,vanilla_bo_hvarfner,1,3300cf54-a184-40c8-bf1a-d019a46cf4c1,12
+1,pest_control_equivalent,vanilla_bo_hvarfner,2,bc15cdba-a5e9-4265-9fc6-2ccee55be589,12
+2,pest_control_equivalent,vanilla_bo_hvarfner,3,88022c51-cc07-48ac-a44b-119a159c0d7a,12
+3,pest_control_equivalent,vanilla_bo_hvarfner,4,ab7b04b8-5be6-4343-b50c-2452bc083adf,12
+4,pest_control_equivalent,vanilla_bo_hvarfner,5,82334229-084b-4fe3-80d0-10c8e595d33e,12
+0,pest_control_equivalent,pr,1,0bdc3a13-bb44-431c-8736-c04856fe9364,12
+1,pest_control_equivalent,pr,2,7fcd0b12-a2b3-4f60-8ae6-107a8627aadb,12
+2,pest_control_equivalent,pr,3,cb77439d-f6ca-4c54-9630-348dad2e13b1,12
+3,pest_control_equivalent,pr,4,437baf45-b3e0-4632-88eb-5233e9f07c17,12
+4,pest_control_equivalent,pr,5,b60c44b3-80a2-4b96-a0c8-0747cbafdd19,12
+0,pest_control_equivalent,saas_bo,1,29f794a4-66fb-4c09-98e7-0219e77890ed,12
+1,pest_control_equivalent,saas_bo,2,fa7bbaa6-35b4-4389-82d7-a830057582a0,12
+2,pest_control_equivalent,saas_bo,3,f7329da5-089d-463d-b78f-39692facbe46,12
+3,pest_control_equivalent,saas_bo,4,07ab5d1f-0159-4ed0-8fa5-ea95008717b3,12
+4,pest_control_equivalent,saas_bo,5,86bd831f-621e-4bb7-a61c-6b325886cf5f,12
+0,rfp_rasp,hill_climbing,1,6c9292f8-1072-43d8-816e-208359f442e1,308
+1,rfp_rasp,hill_climbing,2,e88a45dd-b4b5-4366-a006-dcbebd462143,302
+2,rfp_rasp,hill_climbing,3,25e80be2-f890-4481-be92-4cc7413d8a53,300
+3,rfp_rasp,hill_climbing,4,a3d15a23-677a-4032-ae5b-027f4317778b,309
+4,rfp_rasp,hill_climbing,5,811fc979-8532-4c47-8b6b-8833891eaa75,309
+0,rfp_rasp,genetic_algorithm,1,464d6c23-8be4-4868-9014-45821d724078,296
+1,rfp_rasp,genetic_algorithm,2,250d2961-7634-427b-8ab0-83c05928394c,308
+2,rfp_rasp,genetic_algorithm,3,39a8c36d-0cdc-4f18-b9d6-a85c70bd8a3c,304
+3,rfp_rasp,genetic_algorithm,4,588bcd3b-b066-41e2-968e-72f34b39f007,306
+4,rfp_rasp,genetic_algorithm,5,fc3a6494-81e3-4753-b324-2962fdd8eed3,305
+0,rfp_rasp,cma_es,1,e2e7992a-75f8-403d-907e-f53cb67a6e17,308
+1,rfp_rasp,cma_es,2,fb3fa5f1-501e-486d-8e70-f9f1f7b94854,305
+2,rfp_rasp,cma_es,3,914225ae-995a-4e37-885a-6c7665c3ba91,309
+3,rfp_rasp,cma_es,4,b7cfb251-f4b6-43a1-9ee8-890a536706a0,307
+4,rfp_rasp,cma_es,5,8bee3771-5226-446e-b562-e62ef40338cd,309
+0,rfp_rasp,random_line_bo,1,2b7f85e1-296d-4c75-9aba-e23979a1ddfa,308
+1,rfp_rasp,random_line_bo,2,30ae3e8c-8742-435f-9bc1-c996ad692b23,304
+2,rfp_rasp,random_line_bo,3,614dc316-fd37-4171-9146-6ba70e102922,305
+3,rfp_rasp,random_line_bo,4,7d0d8e9d-3221-4bf1-929d-9871bc96e908,308
+4,rfp_rasp,random_line_bo,5,ef1c6675-1031-4174-b735-21dcd5037747,306
+0,rfp_rasp,coordinate_line_bo,1,28a580b1-0376-40e9-b403-c466ad9e9a58,309
+1,rfp_rasp,coordinate_line_bo,2,010828e3-1b73-41f7-beb5-b2e7ee7b8b06,308
+2,rfp_rasp,coordinate_line_bo,3,e0562e54-468e-4350-8067-8b02b4c16930,304
+3,rfp_rasp,coordinate_line_bo,4,ca3818ae-ea00-4a21-a44e-5cbb15c17388,309
+4,rfp_rasp,coordinate_line_bo,5,387d76cf-4a73-4012-9e64-9e33163c3199,308
+0,rfp_rasp,turbo,1,36b55dd2-ab26-4723-83a5-d0ea9a3263b2,309
+1,rfp_rasp,turbo,2,dfeb6415-b86b-4137-b6b0-466ebf2d1bac,309
+2,rfp_rasp,turbo,3,4bcf0a8b-9f2a-483f-9403-3401e9b2aa89,304
+3,rfp_rasp,turbo,4,52063da0-2e14-479f-833b-56bf6fa44b4f,304
+4,rfp_rasp,turbo,5,3aa84cd7-f7e0-43ee-8ac1-0791d245e58f,303
+0,rfp_rasp,vanilla_bo_hvarfner,1,29dd4a23-ff63-4e86-80f9-19983402523d,309
+1,rfp_rasp,vanilla_bo_hvarfner,2,416a618d-04e6-4c32-87a0-72f78d575150,308
+2,rfp_rasp,vanilla_bo_hvarfner,3,56a3f5bb-0ab2-4b70-bc04-422ec010d138,302
+3,rfp_rasp,vanilla_bo_hvarfner,4,ccbadb3f-f0c1-4ab8-8eaa-35099e503183,297
+4,rfp_rasp,vanilla_bo_hvarfner,5,0c601344-053a-4c0b-927b-9226c4020113,307
+0,rfp_rasp,pr,1,f01a416e-fd8d-417d-bc64-ef10184a20da,306
+1,rfp_rasp,pr,2,2202665d-749d-49ec-a61d-ad6d54ac5aa2,309
+2,rfp_rasp,pr,3,28b832ba-8672-403c-b621-4fb5d7f1787e,308
+3,rfp_rasp,pr,4,6ee5b335-4911-48ab-8c3c-8352dca583e3,308
+4,rfp_rasp,pr,5,ab8c22d3-3189-45b8-b4be-de148623da53,309
+0,rfp_rasp,saas_bo,1,bd545704-a3cc-428a-997a-b7bbd7fcde88,307
+1,rfp_rasp,saas_bo,2,9c757630-bc44-451f-b261-0152ad247bf8,307
+2,rfp_rasp,saas_bo,3,1162a510-0930-459a-8fc6-c706b7b4c843,309
+3,rfp_rasp,saas_bo,4,99ad5444-217d-4eeb-bdad-2273b473f352,308
+4,rfp_rasp,saas_bo,5,34b7e9f7-58b4-42ee-96a2-1758fcd5fa5f,309
diff --git a/pyproject.toml b/pyproject.toml
index 2fee7e7..b71fc6a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,6 +14,7 @@ dependencies = [
     "click",
     "lightning",
     "fair-esm",
+    "jinja2",
     "poli-core@git+https://github.com/MachineLearningLifeScience/poli.git",
     "poli-baselines@git+https://github.com/MachineLearningLifeScience/poli-baselines.git"
 ]
diff --git a/run.py b/run.py
index beefdcf..ef3c974 100644
--- a/run.py
+++ b/run.py
@@ -13,6 +13,7 @@
 from poli.core.exceptions import BudgetExhaustedException
 from poli.core.util.seeding import seed_python_numpy_and_torch
 
+from hdbo_benchmark.utils.constants import WANDB_PROJECT
 from hdbo_benchmark.utils.experiments.load_generative_models import (
     load_generative_model_and_bounds,
 )
@@ -56,7 +57,7 @@ def _main(
         not force_run
         and wandb_mode == "online"
         and experiment_has_already_run(
-            experiment_name="hdbo_benchmark_results",
+            experiment_name=WANDB_PROJECT,
             solver_name=solver_name,
             function_name=function_name,
             n_dimensions=n_dimensions,
@@ -78,7 +79,7 @@ def _main(
 
     # Setting the observer configuration
     observer_config = ObserverConfig(
-        experiment_name="hdbo_benchmark_results",
+        experiment_name=WANDB_PROJECT,
         function_name=function_name,
         solver_name=solver_name,
         n_dimensions=n_dimensions,
diff --git a/src/hdbo_benchmark/results/download_tables.py b/src/hdbo_benchmark/results/download_tables.py
index 7f0f995..72e0edb 100644
--- a/src/hdbo_benchmark/results/download_tables.py
+++ b/src/hdbo_benchmark/results/download_tables.py
@@ -30,6 +30,7 @@
 import wandb
 from hdbo_benchmark.utils.constants import ROOT_DIR
 from hdbo_benchmark.utils.results.download_from_wandb import (
+    get_all_runs_for_experiment,
     get_all_runs_for_function_names,
 )
 
@@ -85,10 +86,33 @@ def create_base_table_for_ehrlich(
     return df
 
 
+def create_base_table_for_entire_benchmark(
+    save_cache: bool = True,
+    use_cache: bool = False,
+    tags: list[str] | None = None,
+):
+    CACHE_PATH = ROOT_DIR / "data" / "results_cache"
+    CACHE_PATH.mkdir(exist_ok=True, parents=True)
+    tags_str = "-".join(tags) if tags is not None else "all"
+    CACHE_FILE = CACHE_PATH / f"base_table_entire_benchmark-tags-{tags_str}.csv"
+
+    if use_cache and CACHE_FILE.exists():
+        df = pd.read_csv(CACHE_FILE)
+        return df
+
+    all_runs = get_all_runs_for_experiment(experiment_name="hdbo-benchmark-results-v2")
+    all_dfs = convert_data_to_dataframes(all_runs)
+
+    df = pd.concat(all_dfs)
+    if save_cache:
+        df.to_csv(CACHE_FILE, index=False)
+
+    return df
+
+
 if __name__ == "__main__":
-    df = create_base_table_for_ehrlich(
+    create_base_table_for_entire_benchmark(
         save_cache=True,
-        use_cache=True,
+        use_cache=False,
         tags=None,
     )
-    print(df)
diff --git a/src/hdbo_benchmark/results/other_results/comparison_between_experimental_setups.py b/src/hdbo_benchmark/results/other_results/comparison_between_experimental_setups.py
index 07bb3e8..3e8b8ca 100644
--- a/src/hdbo_benchmark/results/other_results/comparison_between_experimental_setups.py
+++ b/src/hdbo_benchmark/results/other_results/comparison_between_experimental_setups.py
@@ -9,7 +9,7 @@
 matplotlib.rcParams.update({"text.usetex": True})
 
 initialization = {
-    r"Hvarfner's \texttt{VanillaBO}": [33, 40, 59, 89, 240],
+    r"Hvarfner's": [33, 40, 59, 89, 240],
     r"\texttt{RandomLineBO}": [],
     r"\texttt{SAASBO}": [10, 20],
     r"\texttt{Turbo}": [50, 100, 1000],
@@ -23,7 +23,7 @@
 }
 
 evaluation_budget = {
-    r"Hvarfner's \texttt{VanillaBO}": [100, 200, 500, 1000],
+    r"Hvarfner's": [100, 200, 500, 1000],
     r"\texttt{RandomLineBO}": [1000],
     r"\texttt{BAxUS}": [100, 500, 1000],
     r"\texttt{SAASBO}": [50, 100, 400],
@@ -37,7 +37,7 @@
 }
 
 independent_replications = {
-    r"Hvarfner's \texttt{VanillaBO}": [10, 20],
+    r"Hvarfner's": [10, 20],
     r"\texttt{RandomLineBO}": [100],
     r"\texttt{BAxUS}": [20],
     r"\texttt{SAASBO}": [30],
diff --git a/src/hdbo_benchmark/results/print_tables_for_website_and_paper.py b/src/hdbo_benchmark/results/print_tables_for_website_and_paper.py
index 5aae584..da2a37c 100644
--- a/src/hdbo_benchmark/results/print_tables_for_website_and_paper.py
+++ b/src/hdbo_benchmark/results/print_tables_for_website_and_paper.py
@@ -3,11 +3,14 @@
 """
 
 import json
-
+from typing import Literal
 import numpy as np
 import pandas as pd
 
-from hdbo_benchmark.results.download_tables import create_base_table_for_ehrlich
+from hdbo_benchmark.results.download_tables import (
+    create_base_table_for_ehrlich,
+    create_base_table_for_entire_benchmark,
+)
 from hdbo_benchmark.utils.constants import ROOT_DIR
 
 pd.set_option("display.max_colwidth", None)
@@ -54,18 +57,39 @@ def compute_pretty_names_for_functions():
     }
 
 
-def select_runs(df: pd.DataFrame, function_name: str, solver_name: str) -> pd.DataFrame:
+def select_runs(
+    df: pd.DataFrame,
+    function_name: str,
+    solver_name: str,
+    n_dimensions: Literal[128, 2, None] = None,
+) -> tuple[pd.DataFrame, pd.DataFrame]:
     sliced_df = df[
         (df["function_name"] == function_name) & (df["solver_name"] == solver_name)
     ]
+    if n_dimensions is not None:
+        sliced_df = sliced_df[sliced_df["n_dimensions"] == n_dimensions]
     sliced_df = sliced_df[sliced_df["seed"].isin([1, 2, 3, 4, 5])]
 
     max_iter = 1299 if function_name == "ehrlich_holo_large" else 309
     sliced_df = sliced_df[sliced_df["_step"] <= max_iter]
 
+    # # Saving the longest runs per seed per problem
+    # longest_runs_per_problem_per_seed: dict[str, list[int]] = {}
+    # for function_name in sliced_df["function_name"].unique():
+    #     longest_runs_per_problem_per_seed[function_name] = []
+    #     for seed_ in range(1, 6):
+    #         sliced_df_of_seed = sliced_df[sliced_df["seed"] == seed_]
+    #         longest_run = (
+    #             sliced_df_of_seed.groupby("experiment_id")["_step"].max().nlargest(1)
+    #         )
+    #         longest_runs_per_problem_per_seed[function_name].append(
+    #             longest_run.index[0]
+    #         )
+
     # If the number of experiments is greater than 5, we need to select the "longest" 5,
     # i.e. the ones in which the number of iterations is the greatest (for each seed)
-    if len(sliced_df["experiment_id"].unique()) > 5:
+    rows_for_summary = []
+    if len(sliced_df["experiment_id"].unique()) >= 5:
         slices_per_seed = []
         for seed_ in range(1, 6):
             assert seed_ in sliced_df["seed"].unique()
@@ -76,6 +100,15 @@ def select_runs(df: pd.DataFrame, function_name: str, solver_name: str) -> pd.Da
             print(
                 f"Largest experiment id for {solver_name} in {function_name} (seed {seed_}): {experiment_id_of_longest_runs.index[0]} ({experiment_id_of_longest_runs.values[0]})"
             )
+            rows_for_summary.append(
+                {
+                    "function_name": function_name,
+                    "solver_name": solver_name,
+                    "seed": seed_,
+                    "experiment_id": experiment_id_of_longest_runs.index[0],
+                    "max_steps": experiment_id_of_longest_runs.values[0],
+                }
+            )
             slices_per_seed.append(
                 sliced_df_of_seed[
                     sliced_df_of_seed["experiment_id"]
@@ -89,8 +122,12 @@ def select_runs(df: pd.DataFrame, function_name: str, solver_name: str) -> pd.Da
         assert len(sliced_df["seed"].unique()) == 5
         for i in range(1, 6):
             assert i in sliced_df["seed"].unique()
+    else:
+        print(
+            f"Something's fishy with {function_name} and {solver_name}. Seeds: {sliced_df['seed'].unique()}"
+        )
 
-    return sliced_df
+    return sliced_df, pd.DataFrame(rows_for_summary)
 
 
 def summary_per_function(
@@ -98,14 +135,19 @@ def summary_per_function(
     normalized_per_row: bool = True,
     use_tex: bool = True,
     normalize_with_max_value: float | None = None,
-) -> tuple[pd.DataFrame, pd.DataFrame]:
+    n_dimensions: Literal[128, 2, None] = None,
+) -> tuple[pd.DataFrame, pd.DataFrame, list[dict[str, str]], pd.DataFrame]:
     solver_name_but_pretty = compute_pretty_names_for_solvers(use_tex=use_tex)
 
     rows = []
     missing_experiments = []
+    run_lengths = []
     for function_name in df["function_name"].unique():
         for solver_name in df["solver_name"].unique():
-            slice_df = select_runs(df, function_name, solver_name)
+            slice_df, summary_of_run_lengths = select_runs(
+                df, function_name, solver_name
+            )
+            run_lengths.append(summary_of_run_lengths)
             if (
                 len(slice_df["seed"].unique()) != 5
                 and solver_name in solver_name_but_pretty.keys()
@@ -155,7 +197,7 @@ def summary_per_function(
                 continue
             summary_avg.loc[i] = (row - lowest_value) / (best_value - lowest_value)
 
-    return summary_avg, summary_std, missing_experiments
+    return summary_avg, summary_std, missing_experiments, pd.concat(run_lengths)
 
 
 def print_table_as_tex(
@@ -164,26 +206,45 @@ def print_table_as_tex(
     transpose: bool = False,
     use_tex: bool = True,
     include_color: bool = True,
+    n_dimensions: Literal[128, 2, None] = None,
 ):
     solver_name_but_pretty = compute_pretty_names_for_solvers(use_tex=use_tex)
     problem_name_but_pretty = compute_pretty_names_for_functions()
     index_name = "Solver" + r"\textbackslash " + "Oracle" if transpose else "Oracle"
-    summary_avg, summary_std, missing_experiments = summary_per_function(
-        df, normalized_per_row=normalized
+    summary_avg, summary_std, missing_experiments, run_lengths = summary_per_function(
+        df,
+        normalized_per_row=normalized,
+        n_dimensions=n_dimensions,
     )
-    summary_avg_normalized, _, _ = summary_per_function(
-        df, normalized_per_row=True, normalize_with_max_value=1.0
+    summary_avg_normalized, _, _, _ = summary_per_function(
+        df,
+        normalized_per_row=True,
+        normalize_with_max_value=1.0,
+        n_dimensions=n_dimensions,
     )
 
     final_table_rows: list[dict[str, str]] = []
-    for function_name, pretty_function_name in problem_name_but_pretty.items():
+    for function_name in summary_avg.index:
+        pretty_function_name = problem_name_but_pretty.get(function_name, function_name)
         row = {
             index_name: pretty_function_name,
         }
         for solver_name, pretty_solver_name in solver_name_but_pretty.items():
+            run_lengths_for_solver = run_lengths[
+                (run_lengths["function_name"] == function_name)
+                & (run_lengths["solver_name"] == solver_name)
+            ]
             if solver_name not in summary_avg.columns:
                 row[pretty_solver_name] = r"\alert{[TBD]}"
                 continue
+            if any(run_lengths_for_solver["max_steps"] < 20):
+                print(
+                    f"Some runs for {pretty_solver_name} in {pretty_function_name}"
+                    " have less than 20 steps."
+                    " Will consider them as TBD for now."
+                )
+                row[pretty_solver_name] = r"\alert{[TBD]}"
+                continue
 
             average = summary_avg.loc[function_name, solver_name]
             std = summary_std.loc[function_name, solver_name]
@@ -266,21 +327,50 @@ def print_table_as_tex(
     if transpose:
         final_table = final_table.T
 
-    final_table.to_csv(ROOT_DIR / "data" / "results_cache" / "table_ehrlich.csv")
+    final_table.to_csv(
+        ROOT_DIR / "data" / "results_cache" / f"table_benchmark_{n_dimensions}.csv"
+    )
 
     latex_table = final_table.to_latex(escape=False)
     latex_table = r"\resizebox{\textwidth}{!}{" + latex_table + "}"
 
     print(latex_table)
 
-    with open(ROOT_DIR / "data" / "results_cache" / "table_ehrlich.tex", "w") as f:
+    with open(
+        ROOT_DIR / "data" / "results_cache" / f"table_benchmark_{n_dimensions}.tex",
+        "w",
+    ) as f:
         f.write(latex_table)
 
     with open(
-        ROOT_DIR / "data" / "results_cache" / "missing_experiments_ehrlich.json", "w"
+        ROOT_DIR
+        / "data"
+        / "results_cache"
+        / f"missing_experiments_{n_dimensions}.json",
+        "w",
     ) as f:
         json.dump(missing_experiments, f)
 
+    run_lengths.to_csv(
+        ROOT_DIR / "data" / "results_cache" / f"run_lengths_{n_dimensions}.csv"
+    )
+
+
+def print_table_for_website(
+    use_tex: bool = True,
+    include_color: bool = True,
+    n_dimensions: Literal[128, 2] = 128,
+):
+    df = create_base_table_for_entire_benchmark(save_cache=True, use_cache=True)
+
+    print_table_as_tex(
+        df,
+        transpose=False,
+        use_tex=use_tex,
+        include_color=include_color,
+        n_dimensions=n_dimensions,
+    )
+
 
 def print_table_for_ehrlich(use_tex: bool = True, include_color: bool = True):
     df = create_base_table_for_ehrlich(save_cache=True, use_cache=True)
@@ -294,5 +384,5 @@ def print_table_for_ehrlich(use_tex: bool = True, include_color: bool = True):
 
 
 if __name__ == "__main__":
-    print_table_for_ehrlich(use_tex=True, include_color=True)
-    print_table_for_ehrlich(use_tex=False, include_color=False)
+    print_table_for_website(use_tex=True, include_color=False, n_dimensions=128)
+    print_table_for_website(use_tex=True, include_color=False, n_dimensions=2)
diff --git a/src/hdbo_benchmark/utils/constants.py b/src/hdbo_benchmark/utils/constants.py
index 5200eb3..65b4767 100644
--- a/src/hdbo_benchmark/utils/constants.py
+++ b/src/hdbo_benchmark/utils/constants.py
@@ -14,7 +14,7 @@
 ROOT_DIR = Path(__file__).parent.parent.parent.parent.resolve()
 MODELS_DIR = ROOT_DIR / "data" / "trained_models"
 
-WANDB_PROJECT = "hdbo-embeddings-benchmark"
+WANDB_PROJECT = "hdbo-benchmark-results-v2"
 WANDB_ENTITY = "hdbo-benchmark"
 
 PENALIZE_UNFEASIBLE_WITH = -100.0
diff --git a/src/hdbo_benchmark/utils/experiments/load_problems.py b/src/hdbo_benchmark/utils/experiments/load_problems.py
index 17ba337..c63cb2c 100644
--- a/src/hdbo_benchmark/utils/experiments/load_problems.py
+++ b/src/hdbo_benchmark/utils/experiments/load_problems.py
@@ -115,6 +115,11 @@ def _load_ehrlich_holo(size: Literal["tiny", "small", "large"]) -> Problem:
             n_motifs = 2
             motif_length = 5
             n_supervised_points = 10
+        case "medium":
+            sequence_length = 32
+            n_motifs = 3
+            motif_length = 8
+            n_supervised_points = 50
         case "large":
             sequence_length = 64
             n_motifs = 4
@@ -159,6 +164,8 @@ def _load_problem(function_name: str) -> Problem:
             return _load_ehrlich_holo(size="tiny")
         case "ehrlich_holo_small":
             return _load_ehrlich_holo(size="small")
+        case "ehrlich_holo_medium":
+            return _load_ehrlich_holo(size="medium")
         case "ehrlich_holo_large":
             return _load_ehrlich_holo(size="large")
         case "pest_control_equivalent":
diff --git a/src/hdbo_benchmark/utils/slurm/__init__.py b/src/hdbo_benchmark/utils/slurm/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/hdbo_benchmark/utils/slurm/batch_template.sht b/src/hdbo_benchmark/utils/slurm/batch_template.sht
new file mode 100644
index 0000000..04c4682
--- /dev/null
+++ b/src/hdbo_benchmark/utils/slurm/batch_template.sht
@@ -0,0 +1,16 @@
+#!/bin/bash
+#SBATCH --job-name={{ job_name }}
+#SBATCH --output={{ output_path }}/%j.log
+#SBATCH --error={{ error_path }}/%j.err
+#SBATCH -p gpu {{ gpu_resources }}
+#SBATCH --array=1-{{ line_count }}%{{ parallel_count }}
+#SBATCH --cpus-per-task={{ cpus_per_task }}
+#SBATCH --mem={{ mem }}
+#SBATCH --time={{ time }}
+
+# Get the command from the corresponding line in the file
+command=$(sed -n "${SLURM_ARRAY_TASK_ID}p" {{ commands_file }})
+
+# Execute the command
+echo "Running command: $command"
+eval $command
\ No newline at end of file
diff --git a/src/hdbo_benchmark/utils/slurm/compile_from_missing_experiments.py b/src/hdbo_benchmark/utils/slurm/compile_from_missing_experiments.py
new file mode 100644
index 0000000..69d39e6
--- /dev/null
+++ b/src/hdbo_benchmark/utils/slurm/compile_from_missing_experiments.py
@@ -0,0 +1,88 @@
+import json
+from datetime import datetime
+import pandas as pd
+from typing import Literal
+from hdbo_benchmark.utils.constants import ROOT_DIR
+from hdbo_benchmark.utils.slurm.create_batch_file import (
+    write_batch_script_for_commands,
+)
+
+SOLVER_TO_CONDA_ENV_NAME = {
+    "pr": "hdbo_pr",
+    "vanilla_bo_hvarfner": "hdbo_ax",
+    "saas_bo": "hdbo_ax",
+    "turbo": "hdbo_benchmark",
+}
+
+
+def load_run_lengths(n_dimensions: Literal[128, 2]) -> pd.DataFrame:
+    return pd.read_csv(
+        ROOT_DIR / "data" / "run_lengths" / f"run_lengths_{n_dimensions}.csv"
+    )
+
+
+def compute_experiments_to_run(
+    n_dimensions: Literal[128, 2],
+) -> list[dict[str, list[int]]]:
+    ITERATIONS_THRESHOLD = 20
+
+    run_lengths = load_run_lengths(n_dimensions)
+
+    experiments_to_run = []
+    for experiment in run_lengths.itertuples():
+        if experiment.max_steps <= ITERATIONS_THRESHOLD:
+            experiments_to_run.append(
+                {
+                    "function_name": experiment.function_name,
+                    "solver_name": experiment.solver_name,
+                    "max_steps": experiment.max_steps,
+                    "seed": experiment.seed,
+                    "n_dimensions": n_dimensions,
+                }
+            )
+
+    return experiments_to_run
+
+
+def save_experiments_to_run(experiments_to_run: list[dict[str, list[int]]]):
+    with open(
+        ROOT_DIR / "data" / "results_cache" / "incomplete_experiments.json", "w"
+    ) as f:
+        json.dump(experiments_to_run, f)
+
+
+def from_incomplete_experiments_to_slurm_script(
+    incomplete_experiments: list[dict[str, list[int]]],
+    n_dimensions: Literal[128, 2],
+    *,
+    wandb_mode: str = "online",
+    tag: str = f"incomplete-experiments-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}",
+):
+    commands = []
+    for experiment in incomplete_experiments:
+        max_iter = 300
+        commands.append(
+            f"conda run -n {SOLVER_TO_CONDA_ENV_NAME[experiment['solver_name']]} python run.py --function-name {experiment['function_name']} --solver-name {experiment['solver_name']} --n-dimensions {experiment['n_dimensions']} --seed {experiment['seed']} --max-iter {max_iter} --strict-on-hash --force-run --wandb-mode {wandb_mode} --tag {tag}"
+        )
+
+    write_batch_script_for_commands(
+        commands,
+        job_name=f"incomplete-experiments-{n_dimensions}-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}",
+        gpu_resources="",
+        slurm_script_output_path=ROOT_DIR
+        / f"incomplete-experiments-{n_dimensions}.local.sh",
+        instruction_file_output_path=ROOT_DIR
+        / f"incomplete-experiments-instructions-{n_dimensions}.local.sh",
+    )
+
+
+if __name__ == "__main__":
+    experiments_to_run = compute_experiments_to_run(n_dimensions=128)
+    save_experiments_to_run(experiments_to_run)
+    print(len(experiments_to_run))
+    from_incomplete_experiments_to_slurm_script(experiments_to_run, n_dimensions=128)
+
+    experiments_to_run = compute_experiments_to_run(n_dimensions=2)
+    save_experiments_to_run(experiments_to_run)
+    print(len(experiments_to_run))
+    from_incomplete_experiments_to_slurm_script(experiments_to_run, n_dimensions=2)
diff --git a/src/hdbo_benchmark/utils/slurm/create_batch_file.py b/src/hdbo_benchmark/utils/slurm/create_batch_file.py
new file mode 100644
index 0000000..6597b20
--- /dev/null
+++ b/src/hdbo_benchmark/utils/slurm/create_batch_file.py
@@ -0,0 +1,75 @@
+import os
+import stat
+from pathlib import Path
+
+from jinja2 import Template
+
+from hdbo_benchmark.utils.constants import ROOT_DIR, WANDB_PROJECT
+from hdbo_benchmark.utils.slurm import __file__ as SLURM_UTILS_PATH
+
+
+def read_template():
+    SLURM_UTILS_DIR = Path(SLURM_UTILS_PATH).parent
+    with open(SLURM_UTILS_DIR / "batch_template.sht", "r") as f:
+        return f.read()
+
+
+def write_batch_script_for_commands(
+    commands: list[str],
+    *,
+    job_name: str = "hdbo_benchmark",
+    output_dir: str = f"slurm_logs/{WANDB_PROJECT}",
+    error_dir: str = f"slurm_logs/{WANDB_PROJECT}",
+    gpu_resources: str = "--gres=gpu:titanx:1",
+    cpus_per_task: int = 3,
+    mem: str = "16G",
+    time: str = "23:59:59",
+    parallel_count: int = 1,
+    slurm_script_output_path: Path | str = ROOT_DIR / "batch_script.local.sh",
+    instruction_file_output_path: Path | str = ROOT_DIR / "lines_to_run.local.sh",
+):
+    template_string = read_template()
+
+    # Create a Jinja2 Template object
+    template = Template(template_string)
+
+    # Computing the linecount
+    line_count = len(commands)
+
+    # Writing the instruction file
+    with open(instruction_file_output_path, "w") as fp_instruction:
+        fp_instruction.writelines(
+            [
+                command + "\n" if not command.endswith("\n") else command
+                for command in commands
+            ]
+        )
+
+    # Making sure the output and error dirs exist
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
+    Path(error_dir).mkdir(parents=True, exist_ok=True)
+
+    # Define the values for the placeholders
+    context = {
+        "job_name": job_name,
+        "output_path": output_dir,
+        "error_path": error_dir,
+        "gpu_resources": gpu_resources,
+        "line_count": line_count,
+        "parallel_count": parallel_count,
+        "commands_file": instruction_file_output_path,
+        "cpus_per_task": cpus_per_task,
+        "mem": mem,
+        "time": time,
+    }
+
+    # Render the template with the context
+    rendered_script = template.render(context)
+
+    # Save the rendered script to a file
+    with open(slurm_script_output_path, "w") as fp_slurm_script:
+        fp_slurm_script.write(rendered_script)
+        os.chmod(
+            slurm_script_output_path,
+            os.stat(slurm_script_output_path).st_mode | stat.S_IEXEC,
+        )