diff --git a/mlperf_logging/benchmark_meta.py b/mlperf_logging/benchmark_meta.py
index d323050..cf7c3e3 100644
--- a/mlperf_logging/benchmark_meta.py
+++ b/mlperf_logging/benchmark_meta.py
@@ -20,6 +20,9 @@
         'rgat': 10,  
         'llama2_70b_lora': 10,
         'llama31_405b': 3,
+        # TODO: Update with official values
+        'llama31_8b': 10,
+        'flux1': 10,
     },
     
     'hpc' : {
@@ -143,7 +146,16 @@
         'llama2_70b_lora',
         'rgat',
         'llama31_405b'
-    ]    
+    ],
+    '5.1': [
+        'llama31_8b',
+        'dlrm_dcnv2',   
+        'retinanet',        
+        'flux1',
+        'llama2_70b_lora',
+        'rgat',
+        'llama31_405b'
+    ]  
     },
     
     'hpc': {
diff --git a/mlperf_logging/compliance_checker/README.md b/mlperf_logging/compliance_checker/README.md
index d9f3dee..48c6ed5 100644
--- a/mlperf_logging/compliance_checker/README.md
+++ b/mlperf_logging/compliance_checker/README.md
@@ -12,7 +12,7 @@ To check a log file for compliance:
 
 By default, 5.1.0 training edition rules are used and the default config is set to `5.1.0/common.yaml`.
 This config will check all common keys and enqueue benchmark specific config to be checked as well.
-Old training editions, still supported are 4.0.0, 3.1.0, 3.0.0, 2.1.0, 2.0.0, 1.1.0, 1.0.0, 0.7.0 and 0.6.0
+Old training editions, still supported are 5.0.0, 4.1.0, 4.0.0, 3.1.0, 3.0.0, 2.1.0, 2.0.0, 1.1.0, 1.0.0, 0.7.0 and 0.6.0
 
 To check hpc compliance rules (only 1.0.0 ruleset is supported), set --usage hpc --ruleset 1.0.0.
 
@@ -26,17 +26,19 @@ As log examples use [NVIDIA's training logs](https://github.com/mlperf/training_
     5.1.0/closed_common.yaml   - the common rules file for closed submissions. These rules apply to all benchmarks
     5.1.0/open_common.yaml     - the common rules file for open submissions. These rules apply to all benchmarks
     5.1.0/closed_retinanet.yaml      - Per-benchmark rules, closed submissions.    
-    5.1.0/closed_bert.yaml
+    5.1.0/closed_llama31_8b.yaml
+    5.1.0/closed_llama31_405b.yaml
     5.1.0/closed_dlrm_dcnv2.yaml
     5.1.0/closed_rgat.yaml
     5.1.0/closed_llama2_70b_lora.yaml
-    5.1.0/closed_flux.yaml
+    5.1.0/closed_flux1.yaml
     5.1.0/open_retinanet.yaml        - Per-benchmark rules, open submissions.    
-    5.1.0/open_bert.yaml
+    5.1.0/open_llama31_8b.yaml
+    5.1.0/open_llama31_405b.yaml
     5.1.0/open_dlrm_dcnv2.yaml
     5.1.0/open_rgat.yaml
     5.1.0/open_llama2_70b_lora.yaml
-    5.1.0/open_flux.yaml
+    5.1.0/open_flux1.yaml
 
 ### Existing config files for HPC submissions
 
diff --git a/mlperf_logging/compliance_checker/training_5.1.0/closed_common.yaml b/mlperf_logging/compliance_checker/training_5.1.0/closed_common.yaml
index 2c49169..8639eeb 100755
--- a/mlperf_logging/compliance_checker/training_5.1.0/closed_common.yaml
+++ b/mlperf_logging/compliance_checker/training_5.1.0/closed_common.yaml
@@ -2,7 +2,7 @@
 - KEY:
     NAME:  submission_benchmark
     REQ:   EXACTLY_ONE
-    CHECK: " v['value'] in ['retinanet', 'stable_diffusion', 'dlrm_dcnv2', 'bert', 'rgat', 'llama2_70b_lora', 'flux'] "
+    CHECK: " v['value'] in ['retinanet', 'flux1', 'dlrm_dcnv2', 'llama31_8b', 'rgat', 'llama2_70b_lora', 'llama31_405b'] "
     POST:  " enqueue_config('training_5.1.0/closed_{}.yaml'.format(v['value'])) "
 
 - KEY:
diff --git a/mlperf_logging/compliance_checker/training_5.1.0/closed_flux1.yaml b/mlperf_logging/compliance_checker/training_5.1.0/closed_flux1.yaml
new file mode 100644
index 0000000..23955bc
--- /dev/null
+++ b/mlperf_logging/compliance_checker/training_5.1.0/closed_flux1.yaml
@@ -0,0 +1,58 @@
+# Stable diffusion uses two metrics, FID and CLIP.
+# These metrics can be calculated offline, using different scripts
+# and logged seperatly. Therefore, we create a virtual key
+# called aggregated_eval_accuracy, which aggregates
+# both metrics into a single log line
+
+- KEY:
+    NAME:  global_batch_size
+    REQ:   AT_LEAST_ONE
+    CHECK: " v['value'] >= 0 "
+
+- KEY:
+    NAME:  opt_name
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 'adamw' "
+
+- KEY:
+    NAME:  opt_adamw_beta_1
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0.9 "
+
+- KEY:
+    NAME:  opt_adamw_beta_2
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0.95 "
+
+- KEY:
+    NAME:  opt_adamw_epsilon
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 1e-08 "
+
+- KEY:
+    NAME:  opt_adamw_weight_decay
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0.1 "
+
+- KEY:
+    NAME:  opt_base_learning_rate
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] >= 0.0 "
+
+- KEY:
+    NAME:  opt_learning_rate_warmup_steps
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] >= 0 "
+
+- KEY:
+    NAME:  opt_gradient_clip_norm
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 1.0 "
+
+# TODO: Update with official metric name
+- KEY:
+    NAME: eval_accuracy
+    REQ: AT_LEAST_ONE
+    CHECK:
+        - "'epoch_num' in v['metadata']"
+    ATLEAST_ONE_CHECK: "v['value'] <= 0.586 and v['value'] > 0.0"
diff --git a/mlperf_logging/compliance_checker/training_5.1.0/closed_llama31_405b.yaml b/mlperf_logging/compliance_checker/training_5.1.0/closed_llama31_405b.yaml
new file mode 100644
index 0000000..c47fd87
--- /dev/null
+++ b/mlperf_logging/compliance_checker/training_5.1.0/closed_llama31_405b.yaml
@@ -0,0 +1,85 @@
+- KEY:
+    NAME:  global_batch_size
+    REQ:   EXACTLY_ONE
+    POST: >
+        s['global_batch_size'] = v['value']
+
+- KEY:
+    NAME:  max_sequence_length
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 8192 "
+
+- KEY:
+    NAME:  opt_name
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 'adamw' "
+
+- KEY:
+    NAME:  opt_base_learning_rate
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] * 1152 == s['global_batch_size'] * 8e-5 " 
+
+- KEY:
+    NAME:  opt_end_learning_rate
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_learning_rate_decay_steps
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_learning_rate_warmup_steps
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_learning_rate_decay_schedule
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 'cosine with linear warmup' "
+
+- KEY:
+    NAME:  opt_adamw_beta_1
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0.9 "
+
+- KEY:
+    NAME:  opt_adamw_beta_2
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0.95 "
+
+- KEY:
+    NAME:  opt_adamw_epsilon
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 1e-05 "
+
+- KEY:
+    NAME:  opt_adamw_weight_decay
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 0.1 "
+
+- KEY:
+    NAME:  opt_gradient_clip_norm
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 1.0 "
+
+- KEY:
+    NAME:  gradient_accumulation_steps
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] > 0 "
+
+- KEY:
+    NAME:  eval_samples
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 5760 "
+
+- KEY:
+    NAME:  eval_accuracy
+    REQ:   AT_LEAST_ONE
+    CHECK:
+        - "'samples_count' in v['metadata']"
+    ATLEAST_ONE_CHECK: "(v['value'] <= 5.6) and v['value'] > 0.0"
+
+- KEY:
+    NAME: init_checkpoint_step
+    REQ:  EXACTLY_ONE
+    CHECK: " v['value'] == 0 "
+
diff --git a/mlperf_logging/compliance_checker/training_5.1.0/closed_flux.yaml b/mlperf_logging/compliance_checker/training_5.1.0/closed_llama31_8b.yaml
similarity index 51%
rename from mlperf_logging/compliance_checker/training_5.1.0/closed_flux.yaml
rename to mlperf_logging/compliance_checker/training_5.1.0/closed_llama31_8b.yaml
index d0ed330..3619827 100644
--- a/mlperf_logging/compliance_checker/training_5.1.0/closed_flux.yaml
+++ b/mlperf_logging/compliance_checker/training_5.1.0/closed_llama31_8b.yaml
@@ -4,38 +4,46 @@
     POST: >
         s['global_batch_size'] = v['value']
 
+# TODO: Update with official compliance requirements
+- KEY:
+    NAME:  opt_base_learning_rate
+    REQ:   EXACTLY_ONE
 
 - KEY:
-    NAME:  opt_learning_rate_warmup_steps
+    NAME:  opt_lamb_epsilon
     REQ:   EXACTLY_ONE
 
 - KEY:
-    NAME:  opt_base_learning_rate
+    NAME:  opt_learning_rate_training_steps
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_learning_rate_warmup_steps
     REQ:   EXACTLY_ONE
 
 - KEY:
-    NAME:  opt_gradient_clip_norm
+    NAME:  num_warmup_steps
     REQ:   EXACTLY_ONE
 
 - KEY:
-    NAME:  opt_adamw_weight_decay
+    NAME:  start_warmup_step
     REQ:   EXACTLY_ONE
 
 - KEY:
-    NAME:  opt_adamw_epsilon
+    NAME:  opt_lamb_beta_1
     REQ:   EXACTLY_ONE
 
 - KEY:
-    NAME:  opt_adamw_beta_1
+    NAME:  opt_lamb_beta_2
     REQ:   EXACTLY_ONE
 
 - KEY:
-    NAME:  opt_adamw_beta_2
+    NAME:  opt_lamb_weight_decay_rate
     REQ:   EXACTLY_ONE
 
 - KEY:
     NAME:  eval_accuracy
     REQ:   AT_LEAST_ONE
     CHECK:
-        - "'samples_count' in v['metadata']"
-    ATLEAST_ONE_CHECK: "(v['value'] <= 0.6) and v['value'] > 0.0"
+        - "'epoch_num' in v['metadata']"
+    ATLEAST_ONE_CHECK: "(v['value'] >= 0.720) and v['value'] < 1.0"
diff --git a/mlperf_logging/compliance_checker/training_5.1.0/common.yaml b/mlperf_logging/compliance_checker/training_5.1.0/common.yaml
index cfdd5a6..360854c 100755
--- a/mlperf_logging/compliance_checker/training_5.1.0/common.yaml
+++ b/mlperf_logging/compliance_checker/training_5.1.0/common.yaml
@@ -107,13 +107,13 @@
     NAME:  epoch_start
     REQ:   AT_LEAST_ONE_OR(block_start)
     CHECK:
-        - "'epoch_num' in v['metadata']"
+        - "('epoch_num' in v['metadata']) | ('samples_count' in v['metadata'])"
 
 - KEY:
     NAME:  epoch_stop
     REQ:   AT_LEAST_ONE_OR(block_stop)
     CHECK:
-        - "'epoch_num' in v['metadata']"
+        - "('epoch_num' in v['metadata']) | ('samples_count' in v['metadata'])"
 
 # making sure previous eval did print it's accuracy result
 - KEY:
diff --git a/mlperf_logging/compliance_checker/training_5.1.0/open_common.yaml b/mlperf_logging/compliance_checker/training_5.1.0/open_common.yaml
index 97abafc..41015a8 100644
--- a/mlperf_logging/compliance_checker/training_5.1.0/open_common.yaml
+++ b/mlperf_logging/compliance_checker/training_5.1.0/open_common.yaml
@@ -2,5 +2,5 @@
 - KEY:
     NAME:  submission_benchmark
     REQ:   EXACTLY_ONE
-    CHECK: " v['value'] in ['retinanet', 'dlrm_dcnv2', 'bert', 'rgat', 'llama2_70b_lora', 'flux'] "
+    CHECK: " v['value'] in ['retinanet', 'flux1', 'dlrm_dcnv2', 'llama31_8b', 'rgat', 'llama2_70b_lora', 'llama31_405b'] "
     POST:  " enqueue_config('training_5.1.0/open_{}.yaml'.format(v['value'])) "
diff --git a/mlperf_logging/compliance_checker/training_5.1.0/open_flux.yaml b/mlperf_logging/compliance_checker/training_5.1.0/open_flux.yaml
deleted file mode 100644
index f732825..0000000
--- a/mlperf_logging/compliance_checker/training_5.1.0/open_flux.yaml
+++ /dev/null
@@ -1,6 +0,0 @@
-- KEY:
-    NAME:  eval_accuracy
-    REQ:   AT_LEAST_ONE
-    CHECK:
-        - "'samples_count' in v['metadata']"
-    ATLEAST_ONE_CHECK: "(v['value'] <= 0.6) and v['value'] > 0.0"
diff --git a/mlperf_logging/compliance_checker/training_5.1.0/open_flux1.yaml b/mlperf_logging/compliance_checker/training_5.1.0/open_flux1.yaml
new file mode 100644
index 0000000..4144e05
--- /dev/null
+++ b/mlperf_logging/compliance_checker/training_5.1.0/open_flux1.yaml
@@ -0,0 +1,13 @@
+# Stable diffusion uses two metrics, FID and CLIP.
+# These metrics can be calculated offline, using different scripts
+# and logged seperatly. Therefore, we create a virtual key
+# called aggregated_eval_accuracy, which aggregates
+# both metrics into a single log line
+
+# TODO: Update with official metric name
+- KEY:
+    NAME: averaged_validation_loss
+    REQ: AT_LEAST_ONE
+    CHECK:
+        - "'epoch_num' in v['metadata']"
+    ATLEAST_ONE_CHECK: "v['value'] <= 0.586 and v['value'] > 0.0"
diff --git a/mlperf_logging/compliance_checker/training_5.1.0/open_llama31_405b.yaml b/mlperf_logging/compliance_checker/training_5.1.0/open_llama31_405b.yaml
new file mode 100644
index 0000000..0a29e8b
--- /dev/null
+++ b/mlperf_logging/compliance_checker/training_5.1.0/open_llama31_405b.yaml
@@ -0,0 +1,78 @@
+- KEY:
+    NAME:  global_batch_size
+    REQ:   EXACTLY_ONE
+    POST: >
+        s['global_batch_size'] = v['value']
+
+- KEY:
+    NAME:  max_sequence_length
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 8192 "
+
+- KEY:
+    NAME:  opt_name
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 'adamw' "
+
+- KEY:
+    NAME:  opt_base_learning_rate
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_end_learning_rate
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_learning_rate_decay_steps
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_learning_rate_warmup_steps
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_learning_rate_decay_schedule
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_adamw_beta_1
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_adamw_beta_2
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_adamw_epsilon
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_adamw_weight_decay
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  opt_gradient_clip_norm
+    REQ:   EXACTLY_ONE
+
+- KEY:
+    NAME:  gradient_accumulation_steps
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] > 0 "
+
+- KEY:
+    NAME:  eval_samples
+    REQ:   EXACTLY_ONE
+    CHECK: " v['value'] == 5760 "
+
+- KEY:
+    NAME:  eval_accuracy
+    REQ:   AT_LEAST_ONE
+    CHECK:
+        - "'epoch_num' in v['metadata']"
+    ATLEAST_ONE_CHECK: "(v['value'] <= 5.6) and v['value'] > 0.0"
+
+- KEY:
+    NAME: init_checkpoint_step
+    REQ:  EXACTLY_ONE
+    CHECK: " v['value'] == 0 "
+
diff --git a/mlperf_logging/compliance_checker/training_5.1.0/open_llama31_8b.yaml b/mlperf_logging/compliance_checker/training_5.1.0/open_llama31_8b.yaml
new file mode 100644
index 0000000..ff3f204
--- /dev/null
+++ b/mlperf_logging/compliance_checker/training_5.1.0/open_llama31_8b.yaml
@@ -0,0 +1,8 @@
+
+# TODO: Update with official compliance requirements
+- KEY:
+    NAME:  eval_accuracy
+    REQ:   AT_LEAST_ONE
+    CHECK:
+        - "'epoch_num' in v['metadata']"
+    ATLEAST_ONE_CHECK: "v['value'] < 1.0"
diff --git a/mlperf_logging/mllog/constants.py b/mlperf_logging/mllog/constants.py
index d272c1e..880a814 100644
--- a/mlperf_logging/mllog/constants.py
+++ b/mlperf_logging/mllog/constants.py
@@ -55,6 +55,8 @@
 GNN = "gnn"
 RGAT = "rgat"
 LLAMA31_405B = "llama31_405b"
+LLAMA31_8B = "llama31_8b"
+FLUX1 = "flux1"
 
 # Constant values - model info
 ADAGRAD = "adagrad"
diff --git a/mlperf_logging/rcp_checker/rcp_checker.py b/mlperf_logging/rcp_checker/rcp_checker.py
index 3806a90..c08f4ea 100644
--- a/mlperf_logging/rcp_checker/rcp_checker.py
+++ b/mlperf_logging/rcp_checker/rcp_checker.py
@@ -32,7 +32,7 @@
         'gnn': 10,
         'rgat': 10,  
         'llama2_70b_lora': 10,
-        'flux': 10,
+        'flux1': 10,
         'llama31_405b': 3,
     },
     "hpc": {
@@ -83,7 +83,7 @@ def read_submission_file(result_file, ruleset, use_train_samples):
                     eval_metric = json.loads(eval_accuracy_str)["metadata"]["metric"]
                     eval_score = json.loads(eval_accuracy_str)["value"]
                     stable_diffusion_eval_results[eval_step][eval_metric] = eval_score
-                elif benchmark in {"llama2_70b_lora", "flux", "llama31_405b"} and ("eval_error" in str or "eval_accuracy" in str):
+                elif benchmark in {"llama2_70b_lora", "flux1", "llama31_405b"} and ("eval_error" in str or "eval_accuracy" in str):
                     eval_accuracy_str = str
                     conv_epoch = json.loads(eval_accuracy_str)["metadata"]["samples_count"]
                     eval_score = json.loads(eval_accuracy_str)["value"]
@@ -210,7 +210,7 @@ def _process_raw_rcp_data(self, raw_rcp_data):
         '''
         processed_rcps = {}
         for record, record_contents in raw_rcp_data.items():
-            conv_unit = "samples to converge" if record_contents['Benchmark'] in ['llama2_70b_lora', 'flux'] else "Epochs to converge"
+            conv_unit = "samples to converge" if record_contents['Benchmark'] in ['llama2_70b_lora', 'flux1'] else "Epochs to converge"
             processed_record = {'Benchmark': record_contents['Benchmark'],
                                 'BS': record_contents['BS'],
                                 'Hyperparams': record_contents['Hyperparams'],
@@ -441,7 +441,7 @@ def _set_results_scaling(self, scale_factor, results_dir):
     def _eval_submission_record(self, rcp_record, subm_epochs, results_dir):
         '''Compare reference and submission convergence.'''
 
-        if self.ruleset == "5.0.0" and self.benchmark == "llama31_405b": 
+        if self.ruleset in ["5.0.0", "5.1.0"] and self.benchmark == "llama31_405b": 
             rcp_record['Max Speedup'] = rcp_record['RCP Mean'] / (rcp_record['Min Epochs'] - 46080)
         
         subm_epochs.sort()
diff --git a/mlperf_logging/rcp_checker/training_5.1.0/rcps_flux.json b/mlperf_logging/rcp_checker/training_5.1.0/rcps_flux1.json
similarity index 93%
rename from mlperf_logging/rcp_checker/training_5.1.0/rcps_flux.json
rename to mlperf_logging/rcp_checker/training_5.1.0/rcps_flux1.json
index ba15ef9..3fb9815 100644
--- a/mlperf_logging/rcp_checker/training_5.1.0/rcps_flux.json
+++ b/mlperf_logging/rcp_checker/training_5.1.0/rcps_flux1.json
@@ -1,6 +1,6 @@
 {
   "flux_ref_1024": {
-    "Benchmark": "flux",
+    "Benchmark": "flux1",
     "Creator": "NVIDIA",
     "When": "Reference RCPs before v5.1",
     "Platform": "8xDGX-B200",
@@ -21,7 +21,7 @@
     ]
   },
   "flux_ref_2048": {
-    "Benchmark": "flux",
+    "Benchmark": "flux1",
     "Creator": "NVIDIA",
     "When": "Reference RCPs before v5.1",
     "Platform": "8xDGX-B200",
@@ -42,7 +42,7 @@
     ]
   },
   "flux_ref_4096": {
-    "Benchmark": "flux",
+    "Benchmark": "flux1",
     "Creator": "NVIDIA",
     "When": "Reference RCPs before v5.1",
     "Platform": "8xDGX-B200",
diff --git a/mlperf_logging/rcp_checker/training_5.1.0/rcps_llama31_405b.json b/mlperf_logging/rcp_checker/training_5.1.0/rcps_llama31_405b.json
new file mode 100644
index 0000000..70adaf5
--- /dev/null
+++ b/mlperf_logging/rcp_checker/training_5.1.0/rcps_llama31_405b.json
@@ -0,0 +1,106 @@
+{
+    "llama31_405b_ref_1008":
+    {
+      "Benchmark": "llama31_405b",
+      "Creator": "NVIDIA",
+      "When": "Reference RCPs before 5.0 submission",
+      "Platform": "288xDGX-H100",
+      "BS": 1008,
+      "Hyperparams": {
+        "opt_base_learning_rate": 7e-05,
+        "opt_learning_rate_warmup_steps": 9143,
+        "gradient_accumulation_steps": 126
+      },
+      "Epochs to converge": [
+        324576,324576,324576,
+        324576,324576,324576
+      ]
+    },
+    "llama31_405b_ref_1152":
+    {
+      "Benchmark": "llama31_405b",
+      "Creator": "NVIDIA",
+      "When": "Reference RCPs before 5.0 submission",
+      "Platform": "288xDGX-H100",
+      "BS": 1152,
+      "Hyperparams": {
+        "opt_base_learning_rate": 8e-05,
+        "opt_learning_rate_warmup_steps": 8000,
+        "gradient_accumulation_steps": 144
+      },
+      "Epochs to converge": [
+        322560,322560,322560,
+        322560,322560,322560
+      ]
+    },
+  
+    "llama31_405b_ref_2304":
+    {
+        "Benchmark": "llama31_405b",
+        "Creator": "NVIDIA",
+        "When": "Reference RCPs before 5.0 submission",
+        "Platform": "288xDGX-H100",
+        "BS": 2304,
+        "Hyperparams": {
+          "opt_base_learning_rate": 16e-05,
+          "opt_learning_rate_warmup_steps": 4000,
+          "gradient_accumulation_steps": 288
+        },
+        "Epochs to converge": [
+          368640,368640,368640,
+          368640,414720,414720
+        ]
+      },
+    "llama31_405b_ref_4608":
+    {
+        "Benchmark": "llama31_405b",
+        "Creator": "NVIDIA",
+        "When": "Reference RCPs before 5.0 submission",
+        "Platform": "288xDGX-H100",
+        "BS": 4608,
+        "Hyperparams": {
+          "opt_base_learning_rate": 32e-05,
+          "opt_learning_rate_warmup_steps": 2000,
+          "gradient_accumulation_steps": 576
+        },
+        "Epochs to converge": [
+          460800,460800,506880,
+          506880,506880,506880
+        ]
+      },
+    "llama31_405b_ref_6912":
+    {
+        "Benchmark": "llama31_405b",
+        "Creator": "NVIDIA",
+        "When": "Reference RCPs before 5.0 submission",
+        "Platform": "72xDGX-H100",
+        "BS": 6912,
+        "Hyperparams": {
+          "opt_base_learning_rate": 48e-05,
+          "opt_learning_rate_warmup_steps": 1334,
+          "gradient_accumulation_steps": 3456
+        },
+        "Epochs to converge": [
+          580608,580608,580608,
+          628992,628992,628992
+        ]
+      },
+    "llama31_405b_ref_9216":
+    {
+        "Benchmark": "llama31_405b",
+        "Creator": "NVIDIA",
+        "When": "Reference RCPs before 5.0 submission",
+        "Platform": "288xDGX-H100",
+        "BS": 9216,
+        "Hyperparams": {
+          "opt_base_learning_rate": 64e-05,
+          "opt_learning_rate_warmup_steps": 1000,
+          "gradient_accumulation_steps": 1152
+        },
+        "Epochs to converge": [
+          645120,645120,691200,
+          691200,737280,737280
+        ]
+      }
+  }
+  
\ No newline at end of file
diff --git a/mlperf_logging/rcp_checker/training_5.1.0/rcps_llama31_8b.json b/mlperf_logging/rcp_checker/training_5.1.0/rcps_llama31_8b.json
new file mode 100644
index 0000000..bed1d1c
--- /dev/null
+++ b/mlperf_logging/rcp_checker/training_5.1.0/rcps_llama31_8b.json
@@ -0,0 +1,25 @@
+{
+
+  "llama31_8b_ref_X":
+  {
+    "Benchmark": "",
+    "Creator": "",
+    "When": "",
+    "Platform": "",
+    "BS": 0,
+    "Hyperparams": {
+      "opt_base_learning_rate": 0,
+      "opt_epsilon": 0,
+      "opt_learning_rate_training_steps": 0,
+      "num_warmup_steps": 0,
+      "start_warmup_step": 0,
+      "opt_lamb_beta_1": 0,
+      "opt_lamb_beta_2": 0,
+      "opt_lamb_weight_decay_rate": 0,
+      "gradient_accumulation_steps": 0
+    },
+    "Epochs to converge": [
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+  }
+}
diff --git a/mlperf_logging/rcp_checker/visualization_scripts/rcp_viewer.py b/mlperf_logging/rcp_checker/visualization_scripts/rcp_viewer.py
index 1c4b968..cba24d7 100755
--- a/mlperf_logging/rcp_checker/visualization_scripts/rcp_viewer.py
+++ b/mlperf_logging/rcp_checker/visualization_scripts/rcp_viewer.py
@@ -22,7 +22,7 @@ def main():
     parser.add_argument('--usage', type=str, default='training',
                         choices=['training', 'hpc'],
                         help="the WG that produced the benchmark")
-    parser.add_argument('--version', type=str, default='5.0.0',
+    parser.add_argument('--version', type=str, default='5.1.0',
                         help='what version of the ruleset')
     parser.add_argument('--verbose', action='store_true')
     parser.add_argument('--unpruned', action='store_true',
diff --git a/mlperf_logging/result_summarizer/config.yaml b/mlperf_logging/result_summarizer/config.yaml
index e0306fa..897d29a 100644
--- a/mlperf_logging/result_summarizer/config.yaml
+++ b/mlperf_logging/result_summarizer/config.yaml
@@ -94,12 +94,12 @@ columns:
       llama31_405b: ["Benchmark results (minutes)", "LLM", "C4", "Llama31-405B"]
       default: [" ", " ", " "]
     "5.1.0":
-      bert: ["Benchmark results (minutes)", "NLP", "Wikipedia", "BERT"]
       dlrm_dcnv2: ["Benchmark results (minutes)", "Recommendation", "1TB Multihot Clickthrough", "DLRM DCNv2"]      
       retinanet: ["Benchmark results (minutes)", "Object detection, light-weight", "OpenImages", "RetinaNet"]
-      flux: ["Benchmark results (minutes)", "Text to image", "CC12M and Coco-2014", "Flux"]
+      flux1: ["Benchmark results (minutes)", "Text to image", "CC12M and Coco-2014 for eval", "Flux1"]
       llama2_70b_lora: ["Benchmark results (minutes)", "LLM-Finetune", "SCROLSS Gov Report", "LLama2-70B-LoRA"]
       rgat: ["Benchmark results (minutes)", "Graph node classification", "IGBH-Full", "R-GAT"]
+      llama31_8b: ["Benchmark results (minutes)", "Small LLM", "C4", "Llama31-8b"]
       llama31_405b: ["Benchmark results (minutes)", "LLM", "C4", "Llama31-405B"]
       default: [" ", " ", " "]