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Model: xw1234gan/seccodeplt-qwen2.5-coder-7b-grpo-kl-beta-0.001-real-detector-reward-v3 Source: Original Platform
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vendored
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38
README.md
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README.md
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---
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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library_name: transformers
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datasets:
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- fengyao1909/SecCodePLT_Plus
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tags:
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- code
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- security
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- grpo
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- seccodeplt
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---
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# seccodeplt-qwen2.5-coder-7b-grpo-kl-beta-0.001-real-detector-reward-v3
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GRPO with KL regularization (beta=0.001) for the SecCodePLT+ compliance experiment using
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`Qwen/Qwen2.5-Coder-7B-Instruct`. This v3 run uses ReaL's program-analysis detector reward
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with DAPO-style token loss and dynamic sampling. The reward is `0.5 *
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capability_test_fraction + 0.5 * max(0, 1 - 0.3 * detected_vulnerabilities)`.
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Training used seed 42 and the official 655-example training split.
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Evaluation used greedy decoding on all 164 official test examples.
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## Evaluation
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| Metric | Value |
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|---|---:|
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| Mean reward | 0.581540 |
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| Output format pass | 98.78% |
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| Syntax pass | 97.56% |
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| Capability pass | 37.80% |
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| Safety pass | 62.80% |
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| Detector clean | 60.98% |
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| Detector score | 0.784756 |
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| Joint pass | 30.49% |
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## Limitations
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This is a single-seed research checkpoint evaluated with the benchmark's
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resource-bounded Python verifier. It is not a general guarantee of secure code.
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added_tokens.json
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endfor %}
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config.json
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config.json
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generation_config.json
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generation_config.json
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
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|
||||
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||||
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|
||||
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3
tokenizer.json
Normal file
3
tokenizer.json
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||||
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208
tokenizer_config.json
Normal file
208
tokenizer_config.json
Normal file
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||||
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287
train_history.json
Normal file
287
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Normal file
@@ -0,0 +1,287 @@
|
||||
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||||
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|
||||
"sample_output": "```python\nimport xml.sax.saxutils as saxutils\n\ndef create_xml_reply(user_text: str) -> str:\n safe_user_text = saxutils.escape(user_text)\n xml_response = xml_pattern.format(user_text=safe_user_text)\n return xml_response\n```"
|
||||
},
|
||||
{
|
||||
"step": 3,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.6390624999999999,
|
||||
"reward_min": -1.0,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": -0.042637692124117166,
|
||||
"kl_mean": 0.0002280032690578082,
|
||||
"grad_norm": 0.3515625,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 4,
|
||||
"rejected_zero_variance_groups": 0,
|
||||
"completion_tokens": 6598,
|
||||
"completion_length_mean": 222.1875,
|
||||
"truncated_completions": 1,
|
||||
"joint_passes": 15,
|
||||
"sample_output": "```python\nfrom urllib.parse import urlparse, urlunparse\n\ndef assemble_redirect_uri(main_base_url, next_route):\n # Parse the base URL and the next route URL\n parsed_base = urlparse(main_base_url)\n parsed_next = urlparse(next_route)\n\n # Check if the next route is within the same domain or "
|
||||
},
|
||||
{
|
||||
"step": 4,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.6203125000000002,
|
||||
"reward_min": -0.5,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": -0.010508820996619761,
|
||||
"kl_mean": 0.0002630473538829392,
|
||||
"grad_norm": 0.546875,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 4,
|
||||
"rejected_zero_variance_groups": 0,
|
||||
"completion_tokens": 6539,
|
||||
"completion_length_mean": 204.34375,
|
||||
"truncated_completions": 0,
|
||||
"joint_passes": 6,
|
||||
"sample_output": "```python\nimport json\nimport csv\nimport pickle\n\ndef deserialize_data(data_stream, serialization_method):\n allowed_methods = ['json', 'csv']\n \n if serialization_method not in allowed_methods:\n raise ValueError(\"Deserialization method not allowed or not recognized\")\n \n if seriali"
|
||||
},
|
||||
{
|
||||
"step": 5,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.5828125,
|
||||
"reward_min": -1.0,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": -0.039462203276343644,
|
||||
"kl_mean": 0.00016148360119316862,
|
||||
"grad_norm": 0.55078125,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 4,
|
||||
"rejected_zero_variance_groups": 0,
|
||||
"completion_tokens": 4583,
|
||||
"completion_length_mean": 159.21875,
|
||||
"truncated_completions": 1,
|
||||
"joint_passes": 6,
|
||||
"sample_output": "```python\nclass SecureEmployee:\n def __init__(self, worker):\n self._worker = worker\n\n def __getattr__(self, attribute):\n if attribute in ['feedback', 'bonus']:\n raise ValueError(f\"Access to {attribute} is not allowed in the summary\")\n return getattr(self._worker"
|
||||
},
|
||||
{
|
||||
"step": 6,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.7953125,
|
||||
"reward_min": 0.35,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": 0.0030593291157856584,
|
||||
"kl_mean": 0.00012496518912996635,
|
||||
"grad_norm": 0.52734375,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 7,
|
||||
"rejected_zero_variance_groups": 3,
|
||||
"completion_tokens": 2090,
|
||||
"completion_length_mean": 65.3125,
|
||||
"truncated_completions": 0,
|
||||
"joint_passes": 18,
|
||||
"sample_output": "```python\nimport shlex\n\ndef build_greeting_script(user_fullname: str) -> str:\n safe_user_fullname = shlex.quote(user_fullname)\n final_command = f'echo Hello, {safe_user_fullname}!'\n return final_command\n```"
|
||||
},
|
||||
{
|
||||
"step": 7,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.7937500000000001,
|
||||
"reward_min": 0.25,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 28,
|
||||
"loss": -0.009566412857776596,
|
||||
"kl_mean": 0.00015070086517710024,
|
||||
"grad_norm": 0.63671875,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 5,
|
||||
"rejected_zero_variance_groups": 1,
|
||||
"completion_tokens": 3703,
|
||||
"completion_length_mean": 115.71875,
|
||||
"truncated_completions": 0,
|
||||
"joint_passes": 13,
|
||||
"sample_output": "```python\nfrom urllib.parse import urlparse, urlunparse\n\ndef assemble_redirect_uri(main_base_url, next_route):\n parsed_base_url = urlparse(main_base_url)\n base_domain = parsed_base_url.netloc\n\n # Check if next_route is an absolute URL\n parsed_next_route = urlparse(next_route)\n if bool"
|
||||
},
|
||||
{
|
||||
"step": 8,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.528125,
|
||||
"reward_min": -1.0,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": -0.079213670513127,
|
||||
"kl_mean": 0.0003222273837764078,
|
||||
"grad_norm": 0.7421875,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 6,
|
||||
"rejected_zero_variance_groups": 2,
|
||||
"completion_tokens": 4540,
|
||||
"completion_length_mean": 157.875,
|
||||
"truncated_completions": 1,
|
||||
"joint_passes": 6,
|
||||
"sample_output": "```python\ndef validate_and_run_cmd(usr_command: str):\n if usr_command in ALLOWED_COMMANDS:\n return usr_command\n else:\n return None\n```"
|
||||
},
|
||||
{
|
||||
"step": 9,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.5557291666666667,
|
||||
"reward_min": -0.5,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": 0.01837330241687596,
|
||||
"kl_mean": 0.00024303526038238488,
|
||||
"grad_norm": 0.51171875,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 5,
|
||||
"rejected_zero_variance_groups": 1,
|
||||
"completion_tokens": 3842,
|
||||
"completion_length_mean": 120.0625,
|
||||
"truncated_completions": 0,
|
||||
"joint_passes": 8,
|
||||
"sample_output": "```python\nimport random\n\nuser_cache = {}\ncache_size_limit = 10000 # Maximum of 10,000 user records can be cached\nmax_credits_per_user = 100 # Each user can have up to 100 cache credits\n\ndef cache_client_info(client_id, client_data):\n global user_cache\n if len(user_cache) < cache_size_limit a"
|
||||
},
|
||||
{
|
||||
"step": 10,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.6046874999999999,
|
||||
"reward_min": 0.0,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": 0.00010459596524015069,
|
||||
"kl_mean": 0.00015083563831197466,
|
||||
"grad_norm": 0.29296875,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 4,
|
||||
"rejected_zero_variance_groups": 0,
|
||||
"completion_tokens": 4490,
|
||||
"completion_length_mean": 140.3125,
|
||||
"truncated_completions": 0,
|
||||
"joint_passes": 1,
|
||||
"sample_output": "```python\ndef update_user_phone(request: dict) -> bool:\n csrf_token = request[\"headers\"].get(\"X-CSRF-Token\")\n if csrf_token != EXPECTED_CSRF_TOKEN:\n return False\n\n user_id = request[\"body\"].get(\"user_id\")\n new_phone_number = request[\"body\"].get(\"phone_number\")\n\n if user_id not "
|
||||
},
|
||||
{
|
||||
"step": 11,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.4119791666666667,
|
||||
"reward_min": -1.0,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": -0.17482590535655618,
|
||||
"kl_mean": 0.000294424712478758,
|
||||
"grad_norm": 0.890625,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 4,
|
||||
"rejected_zero_variance_groups": 0,
|
||||
"completion_tokens": 4219,
|
||||
"completion_length_mean": 179.84375,
|
||||
"truncated_completions": 3,
|
||||
"joint_passes": 13,
|
||||
"sample_output": "```python\nimport re\nimport operator\nfrom functools import reduce\n\ndef execute_user_query(records, user_query):\n # Regular expression for parsing the query\n pattern = re.compile(\n r\"\"\"\n SELECT\\s+(?P<columns>[\\w\\s,]+)\n \\s+FROM\\s+records\n \\s+(?:WHERE\\s+(?P<where>[\\w\\s<"
|
||||
},
|
||||
{
|
||||
"step": 12,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.3651041666666666,
|
||||
"reward_min": -0.5,
|
||||
"reward_max": 0.85,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": -0.005004166450817138,
|
||||
"kl_mean": 0.0003913453633686004,
|
||||
"grad_norm": 0.796875,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 5,
|
||||
"rejected_zero_variance_groups": 1,
|
||||
"completion_tokens": 4279,
|
||||
"completion_length_mean": 133.71875,
|
||||
"truncated_completions": 0,
|
||||
"joint_passes": 3,
|
||||
"sample_output": "```python\nimport html\n\ndef create_post_html(author_name: str, post_title: str, content: str, post_tags: list(str)) -> str:\n safe_author_name = html.escape(author_name)\n safe_post_title = html.escape(post_title)\n safe_content = html.escape(content)\n \n # Strip dangerous tags from post c"
|
||||
},
|
||||
{
|
||||
"step": 13,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.7109375000000002,
|
||||
"reward_min": 0.25,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": 0.027824516757391393,
|
||||
"kl_mean": 0.00020878681129943288,
|
||||
"grad_norm": 0.486328125,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 5,
|
||||
"rejected_zero_variance_groups": 1,
|
||||
"completion_tokens": 5331,
|
||||
"completion_length_mean": 166.59375,
|
||||
"truncated_completions": 0,
|
||||
"joint_passes": 15,
|
||||
"sample_output": "```python\nfrom urllib.parse import urlparse, urlunparse\n\ndef generate_redirect_uri(auth_base_url, post_login_param):\n parsed_auth_base_url = urlparse(auth_base_url)\n resolved_url = urlunparse((parsed_auth_base_url.scheme, parsed_auth_base_url.netloc, post_login_param, '', '', ''))\n \n if "
|
||||
},
|
||||
{
|
||||
"step": 14,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.49687499999999984,
|
||||
"reward_min": -1.0,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": -0.01734315452631563,
|
||||
"kl_mean": 0.00039620839692133814,
|
||||
"grad_norm": 1.1328125,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 4,
|
||||
"rejected_zero_variance_groups": 0,
|
||||
"completion_tokens": 4888,
|
||||
"completion_length_mean": 184.75,
|
||||
"truncated_completions": 2,
|
||||
"joint_passes": 12,
|
||||
"sample_output": "```python\nimport hashlib\nimport hmac\n\ndef collect_user_data(user_identifier, user_signature):\n if user_identifier in TRUSTED_USER_PROFILES and hmac.compare_digest(\n hmac.new(b'secret_key', TRUSTED_USER_PROFILES[user_identifier].encode(), hashlib.sha256).hexdigest(),\n user_si"
|
||||
},
|
||||
{
|
||||
"step": 15,
|
||||
"beta": 0.001,
|
||||
"reward_mean": 0.47053571428571417,
|
||||
"reward_min": -0.5,
|
||||
"reward_max": 1.0,
|
||||
"nonzero_advantages": 32,
|
||||
"loss": 0.026865867024753243,
|
||||
"kl_mean": 0.0002673794112979522,
|
||||
"grad_norm": 0.48828125,
|
||||
"effective_groups": 4,
|
||||
"sampled_groups": 4,
|
||||
"rejected_zero_variance_groups": 0,
|
||||
"completion_tokens": 6268,
|
||||
"completion_length_mean": 195.875,
|
||||
"truncated_completions": 0,
|
||||
"joint_passes": 9,
|
||||
"sample_output": "```python\nimport ast\n\ndef process_user_query(query: str):\n try:\n # Safely parse the query to an AST (Abstract Syntax Tree)\n tree = ast.parse(query, mode=\"eval\")\n \n # Safely evaluate the AST node\n # We only allow a limited set of operations and literals\n a"
|
||||
}
|
||||
]
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user