GRPO LoRA (sft=30, grpo=8)

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

adapter_config.json

@@ -0,0 +1,52 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen2ForCausalLM",
+    "parent_library": "transformers.models.qwen2.modeling_qwen2",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "down_proj",
+    "k_proj",
+    "q_proj",
+    "gate_proj",
+    "v_proj",
+    "up_proj",
+    "o_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f686591a0f43b0d8c3093a5c11a3ef439fe1b1b8a5fe4e92800acdf8770be0f6
+size 119801528

repair_library.json

@@ -0,0 +1,910 @@
+{
+  "version": "1",
+  "examples": [
+    {
+      "primitive_type": "ChangeTokenizerBehavior",
+      "breakage_params": {
+        "old_kwarg": "truncation",
+        "old_value": "True",
+        "new_kwarg": "truncate",
+        "new_value": "True"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -16,5 +16,5 @@\n         examples[\"text\"],\n         padding=\"max_length\",\n-        truncate=True,\n+        truncation=True,\n         max_length=64,\n     )\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8691781740179649,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "electra_classification"
+    },
+    {
+      "primitive_type": "ChangeTokenizerBehavior",
+      "breakage_params": {
+        "old_kwarg": "truncation",
+        "old_value": "True",
+        "new_kwarg": "truncate",
+        "new_value": "True"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -16,5 +16,5 @@\n         examples[\"text\"],\n         padding=\"max_length\",\n-        truncate=True,\n+        truncation=True,\n         max_length=64,\n     )\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.7612783886548146,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "electra_classification"
+    },
+    {
+      "primitive_type": "ChangeTokenizerBehavior",
+      "breakage_params": {
+        "old_kwarg": "truncation",
+        "old_value": "True",
+        "new_kwarg": "truncate",
+        "new_value": "True"
+      },
+      "error_signature": "",
+      "repair_diff": "",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.7469754695541743,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "ChangeTokenizerBehavior",
+      "breakage_params": {
+        "old_kwarg": "truncation",
+        "old_value": "True",
+        "new_kwarg": "truncate",
+        "new_value": "True"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -14,5 +14,5 @@\n \n def tokenize_and_align(example):\n-    enc = tokenizer(example[\"tokens\"], is_split_into_words=True, truncate=True, max_length=64)\n+    enc = tokenizer(example[\"tokens\"], is_split_into_words=True, truncation=True, max_length=64)\n     word_ids = enc.word_ids()\n     labels = []\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8811022610483041,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "bert_ner"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "label",
+        "new_column": "labels"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -26,5 +26,5 @@\n         padding=\"max_length\",\n     )\n-    inputs[\"labels\"] = targets[\"input_ids\"]\n+    inputs[\"label\"] = targets[\"input_ids\"]\n     return inputs\n \n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.649018766337638,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "t5_summarization"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "text",
+        "new_column": "input_text"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -26,5 +26,5 @@\n         answer = examples[\"answers\"][i]\n         start_char = answer[\"answer_start\"][0]\n-        end_char = start_char + len(answer[\"input_text\"][0])\n+        end_char = start_char + len(answer[\"text\"][0])\n \n         token_start = next(\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8895669291338583,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "ChangeTokenizerBehavior",
+      "breakage_params": {
+        "old_kwarg": "truncation",
+        "old_value": "True",
+        "new_kwarg": "truncate",
+        "new_value": "True"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -14,5 +14,5 @@\n \n def tokenize_and_align(example):\n-    enc = tokenizer(example[\"tokens\"], is_split_into_words=True, truncate=True, max_length=64)\n+    enc = tokenizer(example[\"tokens\"], is_split_into_words=True, truncation=True, max_length=64)\n     word_ids = enc.word_ids()\n     labels = []\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8010139080581803,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "bert_ner"
+    },
+    {
+      "primitive_type": "ChangeArgumentSignature",
+      "breakage_params": {
+        "function_name": "TrainingArguments",
+        "removed_arg": "num_train_epochs",
+        "added_arg": "max_steps",
+        "added_value": "1000"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -24,4 +24,5 @@\n training_args = TrainingArguments(\n     output_dir=\"/tmp/forge_output/checkpoint\",\n+    num_train_epochs=1,\n     per_device_train_batch_size=4,\n     logging_steps=5,\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8672674881981486,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "gpt2_textgen"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "text",
+        "new_column": "input_text"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -26,5 +26,5 @@\n         answer = examples[\"answers\"][i]\n         start_char = answer[\"answer_start\"][0]\n-        end_char = start_char + len(answer[\"input_text\"][0])\n+        end_char = start_char + len(answer[\"text\"][0])\n \n         token_start = next(\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.5887677670351681,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "RemoveDeprecatedMethod",
+      "breakage_params": {
+        "class_name": "Trainer",
+        "method_name": "save_model",
+        "replacement": "save_to_hub"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -41,4 +41,4 @@\n trainer = Trainer(model=model, args=training_args, train_dataset=dataset)\n trainer.train()\n-trainer.save_model_DEPRECATED(\"/tmp/forge_output/checkpoint\")\n+trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8791026290604065,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "roberta_sentiment"
+    },
+    {
+      "primitive_type": "RenameApiCall",
+      "breakage_params": {
+        "old_name": "trainer.train",
+        "new_name": "trainer.start_training"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -40,5 +40,5 @@\n \n trainer = Trainer(model=model, args=training_args, train_dataset=dataset)\n-trainer.start_training()\n+trainer.train()\n trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.7878403072444018,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "electra_classification"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "text",
+        "new_column": "input_text"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -26,5 +26,5 @@\n         answer = examples[\"answers\"][i]\n         start_char = answer[\"answer_start\"][0]\n-        end_char = start_char + len(answer[\"input_text\"][0])\n+        end_char = start_char + len(answer[\"text\"][0])\n \n         token_start = next(\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8678511447007867,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "text",
+        "new_column": "input_text"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -14,5 +14,5 @@\n def tokenize(examples):\n     return tokenizer(\n-        examples[\"input_text\"],\n+        examples[\"text\"],\n         padding=\"max_length\",\n         truncation=True,\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.6278346817583994,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "roberta_sentiment"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "text",
+        "new_column": "input_text"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -14,5 +14,5 @@\n def tokenize(examples):\n     return tokenizer(\n-        examples[\"input_text\"],\n+        examples[\"text\"],\n         padding=\"max_length\",\n         truncation=True,\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.6966312162081871,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "electra_classification"
+    },
+    {
+      "primitive_type": "ChangeArgumentSignature",
+      "breakage_params": {
+        "function_name": "TrainingArguments",
+        "removed_arg": "num_train_epochs",
+        "added_arg": "max_steps",
+        "added_value": "1000"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -35,4 +35,5 @@\n training_args = TrainingArguments(\n     output_dir=\"/tmp/forge_output/checkpoint\",\n+    num_train_epochs=1,\n     per_device_train_batch_size=16,\n     logging_steps=5,\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.666498939726126,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "distilbert_sst2"
+    },
+    {
+      "primitive_type": "RenameApiCall",
+      "breakage_params": {
+        "old_name": "trainer.train",
+        "new_name": "trainer.start_training"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -63,5 +63,5 @@\n     data_collator=DefaultDataCollator(),\n )\n-trainer.start_training()\n+trainer.train()\n trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.7251096581974675,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "ModifyConfigField",
+      "breakage_params": {
+        "config_class": "TrainingArguments",
+        "field_name": "per_device_train_batch_size",
+        "new_value": "1"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -37,5 +37,5 @@\n     output_dir=\"/tmp/forge_output/checkpoint\",\n     num_train_epochs=1,\n-    per_device_train_batch_size=1,\n+    per_device_train_batch_size=8,\n     logging_steps=5,\n     save_strategy=\"epoch\",\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.817717003567182,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "bert_ner"
+    },
+    {
+      "primitive_type": "ChangeTokenizerBehavior",
+      "breakage_params": {
+        "old_kwarg": "truncation",
+        "old_value": "True",
+        "new_kwarg": "truncate",
+        "new_value": "True"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -16,5 +16,5 @@\n         examples[\"text\"],\n         padding=\"max_length\",\n-        truncate=True,\n+        truncation=True,\n         max_length=64,\n     )\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.7677847401400664,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "roberta_sentiment"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "label",
+        "new_column": "labels"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -16,5 +16,5 @@\n     images = [img.convert(\"RGB\") for img in batch[\"img\"]]\n     inputs = processor(images=images, return_tensors=\"pt\")\n-    inputs[\"labels\"] = torch.tensor(batch[\"labels\"])\n+    inputs[\"labels\"] = torch.tensor(batch[\"label\"])\n     return inputs\n \n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.701744242073817,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "vit_cifar10"
+    },
+    {
+      "primitive_type": "ChangeArgumentSignature",
+      "breakage_params": {
+        "function_name": "TrainingArguments",
+        "removed_arg": "num_train_epochs",
+        "added_arg": "max_steps",
+        "added_value": "1000"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -49,4 +49,5 @@\n training_args = TrainingArguments(\n     output_dir=\"/tmp/forge_output/checkpoint\",\n+    num_train_epochs=1,\n     per_device_train_batch_size=4,\n     logging_steps=5,\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.784986144101346,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "RemoveDeprecatedMethod",
+      "breakage_params": {
+        "class_name": "Trainer",
+        "method_name": "save_model",
+        "replacement": "save_to_hub"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -41,4 +41,4 @@\n trainer = Trainer(model=model, args=training_args, train_dataset=dataset)\n trainer.train()\n-trainer.save_model_DEPRECATED(\"/tmp/forge_output/checkpoint\")\n+trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.6652959989556817,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "electra_classification"
+    },
+    {
+      "primitive_type": "ChangeArgumentSignature",
+      "breakage_params": {
+        "function_name": "TrainingArguments",
+        "removed_arg": "num_train_epochs",
+        "added_arg": "max_steps",
+        "added_value": "1000"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -31,4 +31,5 @@\n training_args = TrainingArguments(\n     output_dir=\"/tmp/forge_output/checkpoint\",\n+    num_train_epochs=1,\n     per_device_train_batch_size=8,\n     logging_steps=5,\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8362977381032284,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "electra_classification"
+    },
+    {
+      "primitive_type": "ChangeTokenizerBehavior",
+      "breakage_params": {
+        "old_kwarg": "truncation",
+        "old_value": "True",
+        "new_kwarg": "truncate",
+        "new_value": "True"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -16,5 +16,5 @@\n         examples[\"text\"],\n         padding=\"max_length\",\n-        truncate=True,\n+        truncation=True,\n         max_length=64,\n     )\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8434749013439302,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "electra_classification"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "text",
+        "new_column": "input_text"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -26,5 +26,5 @@\n         answer = examples[\"answers\"][i]\n         start_char = answer[\"answer_start\"][0]\n-        end_char = start_char + len(answer[\"input_text\"][0])\n+        end_char = start_char + len(answer[\"text\"][0])\n \n         token_start = next(\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.775726750559039,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "ChangeArgumentSignature",
+      "breakage_params": {
+        "function_name": "TrainingArguments",
+        "removed_arg": "num_train_epochs",
+        "added_arg": "max_steps",
+        "added_value": "1000"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -35,4 +35,5 @@\n training_args = TrainingArguments(\n     output_dir=\"/tmp/forge_output/checkpoint\",\n+    num_train_epochs=1,\n     per_device_train_batch_size=16,\n     logging_steps=5,\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.9085137085137085,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "distilbert_sst2"
+    },
+    {
+      "primitive_type": "RenameApiCall",
+      "breakage_params": {
+        "old_name": "trainer.train",
+        "new_name": "trainer.start_training"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -51,5 +51,5 @@\n )\n \n-trainer.start_training()\n+trainer.train()\n trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.7424872199130476,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "bert_ner"
+    },
+    {
+      "primitive_type": "ChangeArgumentSignature",
+      "breakage_params": {
+        "function_name": "TrainingArguments",
+        "removed_arg": "num_train_epochs",
+        "added_arg": "max_steps",
+        "added_value": "1000"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -35,4 +35,5 @@\n training_args = TrainingArguments(\n     output_dir=\"/tmp/forge_output/checkpoint\",\n+    num_train_epochs=1,\n     per_device_train_batch_size=16,\n     logging_steps=5,\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8076153403327943,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "distilbert_sst2"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "text",
+        "new_column": "input_text"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -26,5 +26,5 @@\n         answer = examples[\"answers\"][i]\n         start_char = answer[\"answer_start\"][0]\n-        end_char = start_char + len(answer[\"input_text\"][0])\n+        end_char = start_char + len(answer[\"text\"][0])\n \n         token_start = next(\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8882627677936846,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "RemoveDeprecatedMethod",
+      "breakage_params": {
+        "class_name": "Trainer",
+        "method_name": "save_model",
+        "replacement": "save_to_hub"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -40,4 +40,4 @@\n \n trainer.train()\n-trainer.save_model_DEPRECATED(\"/tmp/forge_output/checkpoint\")\n+trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.5938341205749403,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "gpt2_textgen"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "text",
+        "new_column": "input_text"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -15,5 +15,5 @@\n \n def tokenize(examples):\n-    return tokenizer(examples[\"input_text\"], truncation=True, max_length=64)\n+    return tokenizer(examples[\"text\"], truncation=True, max_length=64)\n \n \n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.6555927441014835,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "gpt2_textgen"
+    },
+    {
+      "primitive_type": "RenameApiCall",
+      "breakage_params": {
+        "old_name": "trainer.train",
+        "new_name": "trainer.start_training"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -63,5 +63,5 @@\n     data_collator=DefaultDataCollator(),\n )\n-trainer.start_training()\n+trainer.train()\n trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.755194754910818,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "RenameApiCall",
+      "breakage_params": {
+        "old_name": "trainer.train",
+        "new_name": "trainer.start_training"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -49,5 +49,5 @@\n )\n \n-trainer.start_training()\n+trainer.train()\n trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8654821132433073,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "distilbert_sst2"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "label",
+        "new_column": "labels"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -16,5 +16,5 @@\n     images = [img.convert(\"RGB\") for img in batch[\"img\"]]\n     inputs = processor(images=images, return_tensors=\"pt\")\n-    inputs[\"labels\"] = torch.tensor(batch[\"labels\"])\n+    inputs[\"labels\"] = torch.tensor(batch[\"label\"])\n     return inputs\n \n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8319525054273182,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "vit_cifar10"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "text",
+        "new_column": "input_text"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -26,5 +26,5 @@\n         answer = examples[\"answers\"][i]\n         start_char = answer[\"answer_start\"][0]\n-        end_char = start_char + len(answer[\"input_text\"][0])\n+        end_char = start_char + len(answer[\"text\"][0])\n \n         token_start = next(\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8109320292832547,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "ModifyConfigField",
+      "breakage_params": {
+        "config_class": "TrainingArguments",
+        "field_name": "per_device_train_batch_size",
+        "new_value": "1"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -36,5 +36,5 @@\n     output_dir=\"/tmp/forge_output/checkpoint\",\n     num_train_epochs=1,\n-    per_device_train_batch_size=1,\n+    per_device_train_batch_size=16,\n     logging_steps=5,\n     save_strategy=\"epoch\",\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8409642541924095,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "distilbert_sst2"
+    },
+    {
+      "primitive_type": "ChangeArgumentSignature",
+      "breakage_params": {
+        "function_name": "TrainingArguments",
+        "removed_arg": "num_train_epochs",
+        "added_arg": "max_steps",
+        "added_value": "1000"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -31,4 +31,5 @@\n training_args = TrainingArguments(\n     output_dir=\"/tmp/forge_output/checkpoint\",\n+    num_train_epochs=1,\n     per_device_train_batch_size=8,\n     logging_steps=5,\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8891815856777494,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "electra_classification"
+    },
+    {
+      "primitive_type": "ModifyConfigField",
+      "breakage_params": {
+        "config_class": "TrainingArguments",
+        "field_name": "per_device_train_batch_size",
+        "new_value": "1"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -29,5 +29,5 @@\n     output_dir=\"/tmp/forge_output/checkpoint\",\n     num_train_epochs=1,\n-    per_device_train_batch_size=1,\n+    per_device_train_batch_size=4,\n     logging_steps=5,\n     save_strategy=\"epoch\",\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.7900720214449505,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "vit_cifar10"
+    },
+    {
+      "primitive_type": "RemoveDeprecatedMethod",
+      "breakage_params": {
+        "class_name": "Trainer",
+        "method_name": "save_model",
+        "replacement": "save_to_hub"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -38,4 +38,4 @@\n trainer = Trainer(model=model, args=training_args, train_dataset=dataset)\n trainer.train()\n-trainer.save_model_DEPRECATED(\"/tmp/forge_output/checkpoint\")\n+trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.7984906001446131,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "vit_cifar10"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "text",
+        "new_column": "input_text"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -26,5 +26,5 @@\n         answer = examples[\"answers\"][i]\n         start_char = answer[\"answer_start\"][0]\n-        end_char = start_char + len(answer[\"input_text\"][0])\n+        end_char = start_char + len(answer[\"text\"][0])\n \n         token_start = next(\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.7808289396602227,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "tokens",
+        "new_column": "words"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -14,5 +14,5 @@\n \n def tokenize_and_align(example):\n-    enc = tokenizer(example[\"words\"], is_split_into_words=True, truncation=True, max_length=64)\n+    enc = tokenizer(example[\"tokens\"], is_split_into_words=True, truncation=True, max_length=64)\n     word_ids = enc.word_ids()\n     labels = []\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.8699562543975037,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "bert_ner"
+    },
+    {
+      "primitive_type": "RenameApiCall",
+      "breakage_params": {
+        "old_name": "trainer.train",
+        "new_name": "trainer.start_training"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -63,5 +63,5 @@\n     data_collator=DefaultDataCollator(),\n )\n-trainer.start_training()\n+trainer.train()\n trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.911495927422025,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "RemoveDeprecatedMethod",
+      "breakage_params": {
+        "class_name": "Trainer",
+        "method_name": "save_model",
+        "replacement": "save_to_hub"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -64,4 +64,4 @@\n )\n trainer.train()\n-trainer.save_model_DEPRECATED(\"/tmp/forge_output/checkpoint\")\n+trainer.save_model(\"/tmp/forge_output/checkpoint\")\n print(\"TRAINING_COMPLETE\")\n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.6131321254553196,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "albert_qa"
+    },
+    {
+      "primitive_type": "RestructureDatasetSchema",
+      "breakage_params": {
+        "old_column": "label",
+        "new_column": "labels"
+      },
+      "error_signature": "",
+      "repair_diff": "--- a/train.py\n+++ b/train.py\n@@ -22,5 +22,5 @@\n \n dataset = dataset.map(tokenize, batched=True)\n-dataset = dataset.rename_column(\"labels\", \"labels\")\n+dataset = dataset.rename_column(\"label\", \"labels\")\n dataset.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n \n",
+      "visible_reward": 1.8,
+      "held_out": {
+        "executed_cleanly": 1.0,
+        "checkpoint_valid": 1.0,
+        "loss_decreased": 0.6040748525323751,
+        "metrics_in_range": 1.0,
+        "no_forbidden_workarounds": 1.0,
+        "intent_preserved": 1.0,
+        "hidden_tests_passed": 1.0
+      },
+      "task_id": "electra_classification"
+    }
+  ],
+  "size": 43,
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tokenizer.json

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+oid sha256:af5891a15588546db1ac7f2baf8fa94835a51a85c032c39793a55bb048b47446
+size 11422523

tokenizer_config.json

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