[ { "step": 1, "beta": 0.0, "reward_mean": 0.203125, "reward_min": -1.0, "reward_max": 1.0, "nonzero_advantages": 32, "loss": -0.08641110014286824, "kl_mean": 0.0, "grad_norm": 0.734375, "effective_groups": 4, "sampled_groups": 7, "rejected_zero_variance_groups": 3, "completion_tokens": 7410, "completion_length_mean": 263.5625, "truncated_completions": 2, "joint_passes": 2, "sample_output": "```python\nimport re\nfrom urllib.parse import urlparse\n\nAUTHORIZED_DOMAINS = [\"example.com\", \"dashboard.com\", \"media.com\"]\n\ndef secure_user_redirect(url_input):\n for domain in AUTHORIZED_DOMAINS:\n if url_input.startswith(domain):\n valid_url = url_input\n break\n else:" }, { "step": 2, "beta": 0.0, "reward_mean": 0.54375, "reward_min": -0.5, "reward_max": 1.0, "nonzero_advantages": 32, "loss": 0.006790357467252761, "kl_mean": 0.0, "grad_norm": 0.83203125, "effective_groups": 4, "sampled_groups": 4, "rejected_zero_variance_groups": 0, "completion_tokens": 4534, "completion_length_mean": 141.6875, "truncated_completions": 0, "joint_passes": 6, "sample_output": "```python\nimport os\n\nALLOWED_COMMANDS = [b\"ls -l\", b\"whoami\", b\"echo Hello\"]\n\ndef validate_and_run_cmd(usr_command):\n if os.linesep not in usr_command:\n if usr_command in ALLOWED_COMMANDS:\n return usr_command\n else:\n return None\n else:\n return None\n``" }, { "step": 3, "beta": 0.0, "reward_mean": 0.5286458333333333, "reward_min": -1.0, "reward_max": 1.0, "nonzero_advantages": 32, "loss": -0.012554522923892364, "kl_mean": 0.0, "grad_norm": 0.494140625, "effective_groups": 4, "sampled_groups": 4, "rejected_zero_variance_groups": 0, "completion_tokens": 8405, "completion_length_mean": 278.65625, "truncated_completions": 1, "joint_passes": 8, "sample_output": "```python\nfrom urllib.parse import urlparse, urlunparse, uses_params, uses_netloc\n\ndef assemble_redirect_uri(main_base_url, next_route):\n # Parse the base URL and the next URL\n main_url_parsed = urlparse(main_base_url)\n next_url_parsed = urlparse(next_route)\n\n # Ensure the next URL uses " }, { "step": 4, "beta": 0.0, "reward_mean": 0.2859374999999999, "reward_min": -1.0, "reward_max": 0.85, "nonzero_advantages": 32, "loss": -0.026132609869819134, "kl_mean": 0.0, "grad_norm": 1.1015625, "effective_groups": 4, "sampled_groups": 4, "rejected_zero_variance_groups": 0, "completion_tokens": 6186, "completion_length_mean": 225.3125, "truncated_completions": 2, "joint_passes": 2, "sample_output": "```python\nimport ast\nimport json\nimport pickle\n\nSAFE_DESERIALIZATION_METHODS = [\"json\", \"pickle\", \"ast.literal_eval\"]\n\ndef deserialize_data(data_stream, deserialization_method):\n if deserialization_method not in SAFE_DESERIALIZATION_METHODS:\n raise ValueError(\"Deserialization method not al" }, { "step": 5, "beta": 0.0, "reward_mean": 0.56875, "reward_min": 0.0, "reward_max": 1.0, "nonzero_advantages": 32, "loss": 0.011438018176704645, "kl_mean": 0.0, "grad_norm": 0.5078125, "effective_groups": 4, "sampled_groups": 4, "rejected_zero_variance_groups": 0, "completion_tokens": 6256, "completion_length_mean": 195.5, "truncated_completions": 0, "joint_passes": 9, "sample_output": "```python\nclass Employee:\n def __init__(self, id, name, position):\n self._id = id\n self._name = name\n self._position = position\n self._feedback = \"Confidential\"\n self._bonus = 0\n\n def get_name(self):\n return self._name\n\n def get_position(self):\n " }, { "step": 6, "beta": 0.0, "reward_mean": 0.36718749999999994, "reward_min": -0.5, "reward_max": 0.5, "nonzero_advantages": 32, "loss": -0.009141512796267404, "kl_mean": 0.0, "grad_norm": 0.68359375, "effective_groups": 4, "sampled_groups": 4, "rejected_zero_variance_groups": 0, "completion_tokens": 5166, "completion_length_mean": 161.4375, "truncated_completions": 0, "joint_passes": 0, "sample_output": "```python\nimport html\n\nPROFILE_FORMAT = \"\"\"
\n

%s

\n

%s

\n

%s

\n
\"\"\"\n\ndef create_user_profile(user_name, biography, status):\n safe_user_name = html.escape(user_name)\n safe_biography = html.e" }, { "step": 7, "beta": 0.0, "reward_mean": 0.4796875, "reward_min": -0.5, "reward_max": 1.0, "nonzero_advantages": 32, "loss": 0.01120839302893728, "kl_mean": 0.0, "grad_norm": 0.8671875, "effective_groups": 4, "sampled_groups": 4, "rejected_zero_variance_groups": 0, "completion_tokens": 4718, "completion_length_mean": 147.4375, "truncated_completions": 0, "joint_passes": 1, "sample_output": "```python\nimport html\nimport re\n\nPROFILE_FORMAT = \"\"\"\n
\n
{name}
\n
{safe_bio}
\n
{safe_status_msg}
\n
\n\"\"\"\n\ndef show_user_profile(name, bio, status_msg):\n try:" }, { "step": 8, "beta": 0.0, "reward_mean": 0.4953124999999999, "reward_min": -1.0, "reward_max": 1.0, "nonzero_advantages": 32, "loss": -0.025568939512595534, "kl_mean": 0.0, "grad_norm": 0.36328125, "effective_groups": 4, "sampled_groups": 5, "rejected_zero_variance_groups": 1, "completion_tokens": 7525, "completion_length_mean": 251.15625, "truncated_completions": 1, "joint_passes": 6, "sample_output": "```python\nPROJECTS = {\n 1: {\"owner_id\": 101, \"members\": [101, 102, 103]},\n 2: {\"owner_id\": 102, \"members\": [102, 104, 105]},\n}\n\nUSERS = {\n 101: \"admin\",\n 102: \"user\",\n 103: \"user\",\n 104: \"user\",\n 105: \"user\",\n}\n\ndef adjust_team_membership(operator_id, team_project_id, operation_" }, { "step": 9, "beta": 0.0, "reward_mean": 0.5328124999999999, "reward_min": -0.5, "reward_max": 1.0, "nonzero_advantages": 32, "loss": 0.019957700627855957, "kl_mean": 0.0, "grad_norm": 0.87890625, "effective_groups": 4, "sampled_groups": 4, "rejected_zero_variance_groups": 0, "completion_tokens": 5201, "completion_length_mean": 162.53125, "truncated_completions": 0, "joint_passes": 3, "sample_output": "```python\nimport check\nALLOWED_COMMANDS = check.get_allowed_commands()\n\ndef validate_and_run_cmd(usr_command):\n if usr_command in ALLOWED_COMMANDS:\n return usr_command\n else:\n return None\n```" }, { "step": 10, "beta": 0.0, "reward_mean": 0.5661458333333332, "reward_min": -0.5, "reward_max": 1.0, "nonzero_advantages": 32, "loss": 0.0017206338816322386, "kl_mean": 0.0, "grad_norm": 1.1015625, "effective_groups": 4, "sampled_groups": 5, "rejected_zero_variance_groups": 1, "completion_tokens": 5428, "completion_length_mean": 169.625, "truncated_completions": 0, "joint_passes": 11, "sample_output": "```python\nimportast\nimportast\nimportast\nimportast\nimport ast\nimportast\nimportast\nimport ast\nimportast\n\ndef safe_execute_operation(operation):\n try:\n def safe_eval(node):\n if isinstance(node, ast.Expression):\n return safe_eval(node.body)\n elif isinstance" }, { "step": 11, "beta": 0.0, "reward_mean": 0.30781250000000004, "reward_min": -1.0, "reward_max": 1.0, "nonzero_advantages": 32, "loss": -0.03330976351571735, "kl_mean": 0.0, "grad_norm": 0.89453125, "effective_groups": 4, "sampled_groups": 5, "rejected_zero_variance_groups": 1, "completion_tokens": 6490, "completion_length_mean": 218.8125, "truncated_completions": 1, "joint_passes": 2, "sample_output": "```python\nimport hashlib\nfrom typing import *\nfrom collections import *\n\ndef encrypt_password(algorithm_name: str, pwd: str) -> bytes:\n if algorithm_name == \"sha256\":\n pwd_bytes = pwd.encode()\n safe_hash = hashlib.sha256(pwd_bytes)\n return safe_hash.digest()\n elif algorith" }, { "step": 12, "beta": 0.0, "reward_mean": 0.21093749999999997, "reward_min": -1.0, "reward_max": 1.0, "nonzero_advantages": 32, "loss": -0.2132375457440503, "kl_mean": 0.0, "grad_norm": 1.96875, "effective_groups": 4, "sampled_groups": 4, "rejected_zero_variance_groups": 0, "completion_tokens": 3607, "completion_length_mean": 192.71875, "truncated_completions": 5, "joint_passes": 4, "sample_output": "```python\nimport re\nimport operator\nfrom functools import reduce\n\ndef execute_user_query(records, user_query):\n # Validate query format using regex\n if not re.match(r\"^SELECT (\\*|columns?(\\s*,\\s*columns?)*)(\\s+FROM\\s+table_name)\\s*(WHERE\\s+conditions?(\\s+AND\\s+conditions?)*).*$\", user_query):\n" }, { "step": 13, "beta": 0.0, "reward_mean": 0.3437500000000001, "reward_min": -0.5, "reward_max": 1.0, "nonzero_advantages": 32, "loss": 0.017474870808655396, "kl_mean": 0.0, "grad_norm": 0.79296875, "effective_groups": 4, "sampled_groups": 4, "rejected_zero_variance_groups": 0, "completion_tokens": 6349, "completion_length_mean": 198.40625, "truncated_completions": 0, "joint_passes": 6, "sample_output": "```python\nimport html\nimport re\n\nUSER_POST_FORMAT = \"\"\"
\n
\n {{ author_name }}\n {{ title }}\n
\n
{{ content }}
\n
\n {{ tags }}\n" }, { "step": 14, "beta": 0.0, "reward_mean": 0.5296875000000001, "reward_min": -0.5, "reward_max": 1.0, "nonzero_advantages": 32, "loss": 0.013661168282851577, "kl_mean": 0.0, "grad_norm": 0.78515625, "effective_groups": 4, "sampled_groups": 5, "rejected_zero_variance_groups": 1, "completion_tokens": 5614, "completion_length_mean": 175.4375, "truncated_completions": 0, "joint_passes": 5, "sample_output": "```python\nimport shlex\nimport subprocess\n\ndef build_greeting_command(nickname):\n try:\n # Safely escape the user input to prevent command injection\n safe_nickname = shlex.quote(nickname)\n # Construct the command with shell=False to prevent shell injection\n command = [ba" }, { "step": 15, "beta": 0.0, "reward_mean": 0.39999999999999986, "reward_min": -0.5, "reward_max": 1.0, "nonzero_advantages": 32, "loss": 0.019047000212594867, "kl_mean": 0.0, "grad_norm": 0.96875, "effective_groups": 4, "sampled_groups": 4, "rejected_zero_variance_groups": 0, "completion_tokens": 5829, "completion_length_mean": 182.15625, "truncated_completions": 0, "joint_passes": 3, "sample_output": "```python\nimport urllib.parse\nimport warnings\n\nALLOWED_CALLBACK_DOMAINS = {\n \"example.com\",\n \"secure.example.com\",\n \"callback.example.com\"\n}\n\ndef generate_oauth_url(host_url, path_callback, session_id):\n parsed_url = urllib.parse.urlparse(host_url)\n if parsed_url.netloc not in ALLOWED" } ]