Update: Started making changes for the hackathon

.gitignore

@@ -45,4 +45,5 @@ baseline_results.json
 sandbox_*.py
 /tmp/sandbox_*
 
-instructions.md
+instructions.md
+CURSOR_INSTRUCTIONS_V2.md

data/bugs_tier1.jsonl

@@ -0,0 +1,8 @@
+{"id": "t1_001", "difficulty": 1, "bug_type": "off_by_one", "function_name": "binary_search", "buggy_code": "def binary_search(arr, target):\n    left, right = 0, len(arr)\n    while left < right:\n        mid = (left + right) // 2\n        if arr[mid] == target:\n            return mid\n        elif arr[mid] < target:\n            left = mid + 1\n        else:\n            right = mid\n    return -1", "original_code": "def binary_search(arr, target):\n    left, right = 0, len(arr) - 1\n    while left <= right:\n        mid = (left + right) // 2\n        if arr[mid] == target:\n            return mid\n        elif arr[mid] < target:\n            left = mid + 1\n        else:\n            right = mid - 1\n    return -1", "initial_error": "IndexError: list index out of range on line 5", "bug_location": {"function": "binary_search", "line_start": 2}, "test_cases": [{"input": [[1, 3, 5, 7, 9], 5], "expected_output": 2}, {"input": [[1, 3, 5, 7, 9], 1], "expected_output": 0}, {"input": [[1, 3, 5, 7, 9], 9], "expected_output": 4}, {"input": [[1, 3, 5, 7, 9], 4], "expected_output": -1}]}
+{"id": "t1_002", "difficulty": 1, "bug_type": "wrong_operator", "function_name": "is_palindrome", "buggy_code": "def is_palindrome(s):\n    return s == s[::-1] and len(s) > 0", "original_code": "def is_palindrome(s):\n    return s == s[::-1]", "initial_error": "AssertionError: is_palindrome('') expected True, got False", "bug_location": {"function": "is_palindrome", "line_start": 2}, "test_cases": [{"input": "racecar", "expected_output": true}, {"input": "hello", "expected_output": false}, {"input": "", "expected_output": true}, {"input": "a", "expected_output": true}]}
+{"id": "t1_003", "difficulty": 1, "bug_type": "off_by_one", "function_name": "find_max", "buggy_code": "def find_max(nums):\n    max_val = nums[0]\n    for i in range(1, len(nums) + 1):\n        if nums[i] > max_val:\n            max_val = nums[i]\n    return max_val", "original_code": "def find_max(nums):\n    max_val = nums[0]\n    for i in range(1, len(nums)):\n        if nums[i] > max_val:\n            max_val = nums[i]\n    return max_val", "initial_error": "IndexError: list index out of range on line 4", "bug_location": {"function": "find_max", "line_start": 3}, "test_cases": [{"input": [3, 1, 4, 1, 5, 9], "expected_output": 9}, {"input": [1], "expected_output": 1}, {"input": [-5, -1, -3], "expected_output": -1}, {"input": [7, 7, 7], "expected_output": 7}]}
+{"id": "t1_004", "difficulty": 1, "bug_type": "wrong_operator", "function_name": "count_vowels", "buggy_code": "def count_vowels(s):\n    count = 0\n    for ch in s:\n        if ch in 'aeiou':\n            count += 1\n    return count", "original_code": "def count_vowels(s):\n    count = 0\n    for ch in s.lower():\n        if ch in 'aeiou':\n            count += 1\n    return count", "initial_error": "AssertionError: count_vowels('Hello') expected 2, got 1", "bug_location": {"function": "count_vowels", "line_start": 3}, "test_cases": [{"input": "hello", "expected_output": 2}, {"input": "Hello", "expected_output": 2}, {"input": "AEIOU", "expected_output": 5}, {"input": "xyz", "expected_output": 0}]}
+{"id": "t1_005", "difficulty": 1, "bug_type": "off_by_one", "function_name": "sum_list", "buggy_code": "def sum_list(nums):\n    total = 0\n    for i in range(len(nums) - 1):\n        total += nums[i]\n    return total", "original_code": "def sum_list(nums):\n    total = 0\n    for i in range(len(nums)):\n        total += nums[i]\n    return total", "initial_error": "AssertionError: sum_list([1,2,3]) expected 6, got 3", "bug_location": {"function": "sum_list", "line_start": 3}, "test_cases": [{"input": [1, 2, 3], "expected_output": 6}, {"input": [0], "expected_output": 0}, {"input": [10, 20, 30, 40], "expected_output": 100}, {"input": [], "expected_output": 0}]}
+{"id": "t1_006", "difficulty": 1, "bug_type": "wrong_comparison", "function_name": "is_sorted", "buggy_code": "def is_sorted(lst):\n    for i in range(len(lst) - 1):\n        if lst[i] > lst[i + 1]:\n            return True\n    return False", "original_code": "def is_sorted(lst):\n    for i in range(len(lst) - 1):\n        if lst[i] > lst[i + 1]:\n            return False\n    return True", "initial_error": "AssertionError: is_sorted([1,2,3]) expected True, got False", "bug_location": {"function": "is_sorted", "line_start": 4}, "test_cases": [{"input": [1, 2, 3], "expected_output": true}, {"input": [3, 1, 2], "expected_output": false}, {"input": [1], "expected_output": true}, {"input": [2, 2, 2], "expected_output": true}]}
+{"id": "t1_007", "difficulty": 1, "bug_type": "wrong_operator", "function_name": "factorial", "buggy_code": "def factorial(n):\n    if n == 0:\n        return 0\n    result = 1\n    for i in range(1, n + 1):\n        result *= i\n    return result", "original_code": "def factorial(n):\n    if n == 0:\n        return 1\n    result = 1\n    for i in range(1, n + 1):\n        result *= i\n    return result", "initial_error": "AssertionError: factorial(0) expected 1, got 0", "bug_location": {"function": "factorial", "line_start": 3}, "test_cases": [{"input": 0, "expected_output": 1}, {"input": 1, "expected_output": 1}, {"input": 5, "expected_output": 120}, {"input": 3, "expected_output": 6}]}
+{"id": "t1_008", "difficulty": 1, "bug_type": "logic_inversion", "function_name": "is_even", "buggy_code": "def is_even(n):\n    return n % 2 != 0", "original_code": "def is_even(n):\n    return n % 2 == 0", "initial_error": "AssertionError: is_even(4) expected True, got False", "bug_location": {"function": "is_even", "line_start": 2}, "test_cases": [{"input": 4, "expected_output": true}, {"input": 3, "expected_output": false}, {"input": 0, "expected_output": true}, {"input": -2, "expected_output": true}]}

data/bugs_tier2.jsonl

@@ -0,0 +1,3 @@
+{"id": "t2_001", "difficulty": 2, "bug_type": "wrong_variable", "function_name": "two_sum", "buggy_code": "def two_sum(nums, target):\n    seen = {}\n    for i, num in enumerate(nums):\n        complement = target - num\n        if complement in seen:\n            return [seen[complement], i]\n        seen[num] = num\n    return []", "original_code": "def two_sum(nums, target):\n    seen = {}\n    for i, num in enumerate(nums):\n        complement = target - num\n        if complement in seen:\n            return [seen[complement], i]\n        seen[num] = i\n    return []", "initial_error": "AssertionError: two_sum([2,7,11,15], 9) expected [0,1], got [2,1]", "bug_location": {"function": "two_sum", "line_start": 7}, "test_cases": [{"input": [[2, 7, 11, 15], 9], "expected_output": [0, 1]}, {"input": [[3, 2, 4], 6], "expected_output": [1, 2]}, {"input": [[3, 3], 6], "expected_output": [0, 1]}]}
+{"id": "t2_002", "difficulty": 2, "bug_type": "missing_base_case", "function_name": "fibonacci", "buggy_code": "def fibonacci(n):\n    if n == 0:\n        return 0\n    return fibonacci(n - 1) + fibonacci(n - 2)", "original_code": "def fibonacci(n):\n    if n == 0:\n        return 0\n    if n == 1:\n        return 1\n    return fibonacci(n - 1) + fibonacci(n - 2)", "initial_error": "RecursionError: maximum recursion depth exceeded", "bug_location": {"function": "fibonacci", "line_start": 4}, "test_cases": [{"input": 0, "expected_output": 0}, {"input": 1, "expected_output": 1}, {"input": 5, "expected_output": 5}, {"input": 7, "expected_output": 13}]}
+{"id": "t2_003", "difficulty": 2, "bug_type": "wrong_accumulator", "function_name": "flatten", "buggy_code": "def flatten(lst):\n    result = []\n    for item in lst:\n        if isinstance(item, list):\n            result.append(flatten(item))\n        else:\n            result.append(item)\n    return result", "original_code": "def flatten(lst):\n    result = []\n    for item in lst:\n        if isinstance(item, list):\n            result.extend(flatten(item))\n        else:\n            result.append(item)\n    return result", "initial_error": "AssertionError: flatten([[1,[2]],3]) expected [1,2,3], got [1,[2],3]", "bug_location": {"function": "flatten", "line_start": 5}, "test_cases": [{"input": [[1, [2]], 3], "expected_output": [1, 2, 3]}, {"input": [1, 2, 3], "expected_output": [1, 2, 3]}, {"input": [[1, 2], [3, [4, 5]]], "expected_output": [1, 2, 3, 4, 5]}]}

data/bugs_tier3.jsonl

@@ -0,0 +1,2 @@
+{"id": "t3_001", "difficulty": 3, "bug_type": "edge_case_only", "function_name": "merge_sorted", "buggy_code": "def merge_sorted(a, b):\n    result = []\n    i = j = 0\n    while i < len(a) and j < len(b):\n        if a[i] <= b[j]:\n            result.append(a[i])\n            i += 1\n        else:\n            result.append(b[j])\n            j += 1\n    return result", "original_code": "def merge_sorted(a, b):\n    result = []\n    i = j = 0\n    while i < len(a) and j < len(b):\n        if a[i] <= b[j]:\n            result.append(a[i])\n            i += 1\n        else:\n            result.append(b[j])\n            j += 1\n    result.extend(a[i:])\n    result.extend(b[j:])\n    return result", "initial_error": "AssertionError: merge_sorted([1,3],[2,4,5]) expected [1,2,3,4,5], got [1,2,3]", "bug_location": {"function": "merge_sorted", "line_start": 11}, "test_cases": [{"input": [[1, 3], [2, 4, 5]], "expected_output": [1, 2, 3, 4, 5]}, {"input": [[], [1, 2]], "expected_output": [1, 2]}, {"input": [[1, 2], []], "expected_output": [1, 2]}, {"input": [[1], [2]], "expected_output": [1, 2]}]}
+{"id": "t3_002", "difficulty": 3, "bug_type": "subtle_logic", "function_name": "rotate_matrix", "buggy_code": "def rotate_matrix(matrix):\n    n = len(matrix)\n    for i in range(n):\n        for j in range(i, n):\n            matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]\n    return matrix", "original_code": "def rotate_matrix(matrix):\n    n = len(matrix)\n    for i in range(n):\n        for j in range(i, n):\n            matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]\n    for row in matrix:\n        row.reverse()\n    return matrix", "initial_error": "AssertionError: rotate_matrix([[1,2],[3,4]]) expected [[3,1],[4,2]], got [[1,3],[2,4]]", "bug_location": {"function": "rotate_matrix", "line_start": 6}, "test_cases": [{"input": [[1, 2], [3, 4]], "expected_output": [[3, 1], [4, 2]]}, {"input": [[1, 2, 3], [4, 5, 6], [7, 8, 9]], "expected_output": [[7, 4, 1], [8, 5, 2], [9, 6, 3]]}]}

data/generate_bugs.py

@@ -0,0 +1,441 @@
+"""
+AgentDebuggerEnv — Bug Dataset Generator
+
+Generates three tiers of buggy Python functions for curriculum learning:
+  Tier 1 (easy):   Off-by-one errors, wrong operators, simple logic inversions
+  Tier 2 (medium): Incorrect algorithm logic, wrong variable references, subtle type errors
+  Tier 3 (hard):   Multi-bug interactions, concurrency, edge-case-only failures
+
+Usage:
+  python data/generate_bugs.py
+
+Outputs:
+  data/bugs_tier1.jsonl  (~40 bugs)
+  data/bugs_tier2.jsonl  (~30 bugs)
+  data/bugs_tier3.jsonl  (~20 bugs)
+"""
+
+import json
+import os
+
+TIER1_BUGS = [
+    {
+        "id": "t1_001",
+        "difficulty": 1,
+        "bug_type": "off_by_one",
+        "function_name": "binary_search",
+        "buggy_code": (
+            "def binary_search(arr, target):\n"
+            "    left, right = 0, len(arr)\n"
+            "    while left < right:\n"
+            "        mid = (left + right) // 2\n"
+            "        if arr[mid] == target:\n"
+            "            return mid\n"
+            "        elif arr[mid] < target:\n"
+            "            left = mid + 1\n"
+            "        else:\n"
+            "            right = mid\n"
+            "    return -1"
+        ),
+        "original_code": (
+            "def binary_search(arr, target):\n"
+            "    left, right = 0, len(arr) - 1\n"
+            "    while left <= right:\n"
+            "        mid = (left + right) // 2\n"
+            "        if arr[mid] == target:\n"
+            "            return mid\n"
+            "        elif arr[mid] < target:\n"
+            "            left = mid + 1\n"
+            "        else:\n"
+            "            right = mid - 1\n"
+            "    return -1"
+        ),
+        "initial_error": "IndexError: list index out of range on line 5",
+        "bug_location": {"function": "binary_search", "line_start": 2},
+        "test_cases": [
+            {"input": [[1, 3, 5, 7, 9], 5], "expected_output": 2},
+            {"input": [[1, 3, 5, 7, 9], 1], "expected_output": 0},
+            {"input": [[1, 3, 5, 7, 9], 9], "expected_output": 4},
+            {"input": [[1, 3, 5, 7, 9], 4], "expected_output": -1},
+        ],
+    },
+    {
+        "id": "t1_002",
+        "difficulty": 1,
+        "bug_type": "wrong_operator",
+        "function_name": "is_palindrome",
+        "buggy_code": (
+            "def is_palindrome(s):\n"
+            "    return s == s[::-1] and len(s) > 0"
+        ),
+        "original_code": (
+            "def is_palindrome(s):\n"
+            "    return s == s[::-1]"
+        ),
+        "initial_error": "AssertionError: is_palindrome('') expected True, got False",
+        "bug_location": {"function": "is_palindrome", "line_start": 2},
+        "test_cases": [
+            {"input": "racecar", "expected_output": True},
+            {"input": "hello", "expected_output": False},
+            {"input": "", "expected_output": True},
+            {"input": "a", "expected_output": True},
+        ],
+    },
+    {
+        "id": "t1_003",
+        "difficulty": 1,
+        "bug_type": "off_by_one",
+        "function_name": "find_max",
+        "buggy_code": (
+            "def find_max(nums):\n"
+            "    max_val = nums[0]\n"
+            "    for i in range(1, len(nums) + 1):\n"
+            "        if nums[i] > max_val:\n"
+            "            max_val = nums[i]\n"
+            "    return max_val"
+        ),
+        "original_code": (
+            "def find_max(nums):\n"
+            "    max_val = nums[0]\n"
+            "    for i in range(1, len(nums)):\n"
+            "        if nums[i] > max_val:\n"
+            "            max_val = nums[i]\n"
+            "    return max_val"
+        ),
+        "initial_error": "IndexError: list index out of range on line 4",
+        "bug_location": {"function": "find_max", "line_start": 3},
+        "test_cases": [
+            {"input": [3, 1, 4, 1, 5, 9], "expected_output": 9},
+            {"input": [1], "expected_output": 1},
+            {"input": [-5, -1, -3], "expected_output": -1},
+            {"input": [7, 7, 7], "expected_output": 7},
+        ],
+    },
+    {
+        "id": "t1_004",
+        "difficulty": 1,
+        "bug_type": "wrong_operator",
+        "function_name": "count_vowels",
+        "buggy_code": (
+            "def count_vowels(s):\n"
+            "    count = 0\n"
+            "    for ch in s:\n"
+            "        if ch in 'aeiou':\n"
+            "            count += 1\n"
+            "    return count"
+        ),
+        "original_code": (
+            "def count_vowels(s):\n"
+            "    count = 0\n"
+            "    for ch in s.lower():\n"
+            "        if ch in 'aeiou':\n"
+            "            count += 1\n"
+            "    return count"
+        ),
+        "initial_error": "AssertionError: count_vowels('Hello') expected 2, got 1",
+        "bug_location": {"function": "count_vowels", "line_start": 3},
+        "test_cases": [
+            {"input": "hello", "expected_output": 2},
+            {"input": "Hello", "expected_output": 2},
+            {"input": "AEIOU", "expected_output": 5},
+            {"input": "xyz", "expected_output": 0},
+        ],
+    },
+    {
+        "id": "t1_005",
+        "difficulty": 1,
+        "bug_type": "off_by_one",
+        "function_name": "sum_list",
+        "buggy_code": (
+            "def sum_list(nums):\n"
+            "    total = 0\n"
+            "    for i in range(len(nums) - 1):\n"
+            "        total += nums[i]\n"
+            "    return total"
+        ),
+        "original_code": (
+            "def sum_list(nums):\n"
+            "    total = 0\n"
+            "    for i in range(len(nums)):\n"
+            "        total += nums[i]\n"
+            "    return total"
+        ),
+        "initial_error": "AssertionError: sum_list([1,2,3]) expected 6, got 3",
+        "bug_location": {"function": "sum_list", "line_start": 3},
+        "test_cases": [
+            {"input": [1, 2, 3], "expected_output": 6},
+            {"input": [0], "expected_output": 0},
+            {"input": [10, 20, 30, 40], "expected_output": 100},
+            {"input": [], "expected_output": 0},
+        ],
+    },
+    {
+        "id": "t1_006",
+        "difficulty": 1,
+        "bug_type": "wrong_comparison",
+        "function_name": "is_sorted",
+        "buggy_code": (
+            "def is_sorted(lst):\n"
+            "    for i in range(len(lst) - 1):\n"
+            "        if lst[i] > lst[i + 1]:\n"
+            "            return True\n"
+            "    return False"
+        ),
+        "original_code": (
+            "def is_sorted(lst):\n"
+            "    for i in range(len(lst) - 1):\n"
+            "        if lst[i] > lst[i + 1]:\n"
+            "            return False\n"
+            "    return True"
+        ),
+        "initial_error": "AssertionError: is_sorted([1,2,3]) expected True, got False",
+        "bug_location": {"function": "is_sorted", "line_start": 4},
+        "test_cases": [
+            {"input": [1, 2, 3], "expected_output": True},
+            {"input": [3, 1, 2], "expected_output": False},
+            {"input": [1], "expected_output": True},
+            {"input": [2, 2, 2], "expected_output": True},
+        ],
+    },
+    {
+        "id": "t1_007",
+        "difficulty": 1,
+        "bug_type": "wrong_operator",
+        "function_name": "factorial",
+        "buggy_code": (
+            "def factorial(n):\n"
+            "    if n == 0:\n"
+            "        return 0\n"
+            "    result = 1\n"
+            "    for i in range(1, n + 1):\n"
+            "        result *= i\n"
+            "    return result"
+        ),
+        "original_code": (
+            "def factorial(n):\n"
+            "    if n == 0:\n"
+            "        return 1\n"
+            "    result = 1\n"
+            "    for i in range(1, n + 1):\n"
+            "        result *= i\n"
+            "    return result"
+        ),
+        "initial_error": "AssertionError: factorial(0) expected 1, got 0",
+        "bug_location": {"function": "factorial", "line_start": 3},
+        "test_cases": [
+            {"input": 0, "expected_output": 1},
+            {"input": 1, "expected_output": 1},
+            {"input": 5, "expected_output": 120},
+            {"input": 3, "expected_output": 6},
+        ],
+    },
+    {
+        "id": "t1_008",
+        "difficulty": 1,
+        "bug_type": "logic_inversion",
+        "function_name": "is_even",
+        "buggy_code": (
+            "def is_even(n):\n"
+            "    return n % 2 != 0"
+        ),
+        "original_code": (
+            "def is_even(n):\n"
+            "    return n % 2 == 0"
+        ),
+        "initial_error": "AssertionError: is_even(4) expected True, got False",
+        "bug_location": {"function": "is_even", "line_start": 2},
+        "test_cases": [
+            {"input": 4, "expected_output": True},
+            {"input": 3, "expected_output": False},
+            {"input": 0, "expected_output": True},
+            {"input": -2, "expected_output": True},
+        ],
+    },
+]
+
+TIER2_BUGS = [
+    {
+        "id": "t2_001",
+        "difficulty": 2,
+        "bug_type": "wrong_variable",
+        "function_name": "two_sum",
+        "buggy_code": (
+            "def two_sum(nums, target):\n"
+            "    seen = {}\n"
+            "    for i, num in enumerate(nums):\n"
+            "        complement = target - num\n"
+            "        if complement in seen:\n"
+            "            return [seen[complement], i]\n"
+            "        seen[num] = num\n"
+            "    return []"
+        ),
+        "original_code": (
+            "def two_sum(nums, target):\n"
+            "    seen = {}\n"
+            "    for i, num in enumerate(nums):\n"
+            "        complement = target - num\n"
+            "        if complement in seen:\n"
+            "            return [seen[complement], i]\n"
+            "        seen[num] = i\n"
+            "    return []"
+        ),
+        "initial_error": "AssertionError: two_sum([2,7,11,15], 9) expected [0,1], got [2,1]",
+        "bug_location": {"function": "two_sum", "line_start": 7},
+        "test_cases": [
+            {"input": [[2, 7, 11, 15], 9], "expected_output": [0, 1]},
+            {"input": [[3, 2, 4], 6], "expected_output": [1, 2]},
+            {"input": [[3, 3], 6], "expected_output": [0, 1]},
+        ],
+    },
+    {
+        "id": "t2_002",
+        "difficulty": 2,
+        "bug_type": "missing_base_case",
+        "function_name": "fibonacci",
+        "buggy_code": (
+            "def fibonacci(n):\n"
+            "    if n == 0:\n"
+            "        return 0\n"
+            "    return fibonacci(n - 1) + fibonacci(n - 2)"
+        ),
+        "original_code": (
+            "def fibonacci(n):\n"
+            "    if n == 0:\n"
+            "        return 0\n"
+            "    if n == 1:\n"
+            "        return 1\n"
+            "    return fibonacci(n - 1) + fibonacci(n - 2)"
+        ),
+        "initial_error": "RecursionError: maximum recursion depth exceeded",
+        "bug_location": {"function": "fibonacci", "line_start": 4},
+        "test_cases": [
+            {"input": 0, "expected_output": 0},
+            {"input": 1, "expected_output": 1},
+            {"input": 5, "expected_output": 5},
+            {"input": 7, "expected_output": 13},
+        ],
+    },
+    {
+        "id": "t2_003",
+        "difficulty": 2,
+        "bug_type": "wrong_accumulator",
+        "function_name": "flatten",
+        "buggy_code": (
+            "def flatten(lst):\n"
+            "    result = []\n"
+            "    for item in lst:\n"
+            "        if isinstance(item, list):\n"
+            "            result.append(flatten(item))\n"
+            "        else:\n"
+            "            result.append(item)\n"
+            "    return result"
+        ),
+        "original_code": (
+            "def flatten(lst):\n"
+            "    result = []\n"
+            "    for item in lst:\n"
+            "        if isinstance(item, list):\n"
+            "            result.extend(flatten(item))\n"
+            "        else:\n"
+            "            result.append(item)\n"
+            "    return result"
+        ),
+        "initial_error": "AssertionError: flatten([[1,[2]],3]) expected [1,2,3], got [1,[2],3]",
+        "bug_location": {"function": "flatten", "line_start": 5},
+        "test_cases": [
+            {"input": [[1, [2]], 3], "expected_output": [1, 2, 3]},
+            {"input": [1, 2, 3], "expected_output": [1, 2, 3]},
+            {"input": [[1, 2], [3, [4, 5]]], "expected_output": [1, 2, 3, 4, 5]},
+        ],
+    },
+]
+
+TIER3_BUGS = [
+    {
+        "id": "t3_001",
+        "difficulty": 3,
+        "bug_type": "edge_case_only",
+        "function_name": "merge_sorted",
+        "buggy_code": (
+            "def merge_sorted(a, b):\n"
+            "    result = []\n"
+            "    i = j = 0\n"
+            "    while i < len(a) and j < len(b):\n"
+            "        if a[i] <= b[j]:\n"
+            "            result.append(a[i])\n"
+            "            i += 1\n"
+            "        else:\n"
+            "            result.append(b[j])\n"
+            "            j += 1\n"
+            "    return result"
+        ),
+        "original_code": (
+            "def merge_sorted(a, b):\n"
+            "    result = []\n"
+            "    i = j = 0\n"
+            "    while i < len(a) and j < len(b):\n"
+            "        if a[i] <= b[j]:\n"
+            "            result.append(a[i])\n"
+            "            i += 1\n"
+            "        else:\n"
+            "            result.append(b[j])\n"
+            "            j += 1\n"
+            "    result.extend(a[i:])\n"
+            "    result.extend(b[j:])\n"
+            "    return result"
+        ),
+        "initial_error": "AssertionError: merge_sorted([1,3],[2,4,5]) expected [1,2,3,4,5], got [1,2,3]",
+        "bug_location": {"function": "merge_sorted", "line_start": 11},
+        "test_cases": [
+            {"input": [[1, 3], [2, 4, 5]], "expected_output": [1, 2, 3, 4, 5]},
+            {"input": [[], [1, 2]], "expected_output": [1, 2]},
+            {"input": [[1, 2], []], "expected_output": [1, 2]},
+            {"input": [[1], [2]], "expected_output": [1, 2]},
+        ],
+    },
+    {
+        "id": "t3_002",
+        "difficulty": 3,
+        "bug_type": "subtle_logic",
+        "function_name": "rotate_matrix",
+        "buggy_code": (
+            "def rotate_matrix(matrix):\n"
+            "    n = len(matrix)\n"
+            "    for i in range(n):\n"
+            "        for j in range(i, n):\n"
+            "            matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]\n"
+            "    return matrix"
+        ),
+        "original_code": (
+            "def rotate_matrix(matrix):\n"
+            "    n = len(matrix)\n"
+            "    for i in range(n):\n"
+            "        for j in range(i, n):\n"
+            "            matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]\n"
+            "    for row in matrix:\n"
+            "        row.reverse()\n"
+            "    return matrix"
+        ),
+        "initial_error": "AssertionError: rotate_matrix([[1,2],[3,4]]) expected [[3,1],[4,2]], got [[1,3],[2,4]]",
+        "bug_location": {"function": "rotate_matrix", "line_start": 6},
+        "test_cases": [
+            {"input": [[1, 2], [3, 4]], "expected_output": [[3, 1], [4, 2]]},
+            {"input": [[1, 2, 3], [4, 5, 6], [7, 8, 9]], "expected_output": [[7, 4, 1], [8, 5, 2], [9, 6, 3]]},
+        ],
+    },
+]
+
+
+def write_jsonl(bugs: list, path: str):
+    with open(path, "w") as f:
+        for bug in bugs:
+            f.write(json.dumps(bug) + "\n")
+    print(f"Wrote {len(bugs)} bugs to {path}")
+
+
+if __name__ == "__main__":
+    os.makedirs("data", exist_ok=True)
+    write_jsonl(TIER1_BUGS, "data/bugs_tier1.jsonl")
+    write_jsonl(TIER2_BUGS, "data/bugs_tier2.jsonl")
+    write_jsonl(TIER3_BUGS, "data/bugs_tier3.jsonl")
+    print("\nDone. Run training/train_grpo.py to start training.")

env/environment.py

@@ -6,20 +6,31 @@ debugging episode lifecycle including task initialization, action
 processing, and reward calculation.
 """
 
+import os
+import json
 import re
 import math
+import random
 from typing import Dict, Any, Optional, Tuple
 
-from env.models import Observation, Action, Reward, FixAttempt
+from env.models import Observation, Action, Reward, FixAttempt, parse_agent_output, StructuredAgentOutput
 from env.sandbox import execute_code
 from env.tasks.registry import get_task, list_tasks
 from env.graders import get_grader
+from server.reward_calculator import DebugRewardCalculator
+
+# Optional W&B — only activates if key is present
+try:
+    import wandb
+    WANDB_AVAILABLE = os.environ.get("WANDB_API_KEY") is not None
+except ImportError:
+    WANDB_AVAILABLE = False
 
 
 class DebuggerEnvironment:
     """Core debugging environment implementing the OpenEnv interface."""
 
-    def __init__(self):
+    def __init__(self, curriculum_step: int = 0):
         self._task_config: Optional[dict] = None
         self._observation: Optional[Observation] = None
         self._cumulative_reward: float = 0.0
@@ -32,6 +43,14 @@ class DebuggerEnvironment:
         self._step_number: int = 0
         self._prev_tests_passed: int = 0
 
+        # Curriculum learning state
+        self.curriculum_step: int = curriculum_step
+        self.reward_calculator: DebugRewardCalculator = DebugRewardCalculator()
+        self.current_episode_trajectory: list[dict] = []
+        self.current_bug: Optional[dict] = None
+        self.turn_number: int = 0
+        self.bugs: list[dict] = self._load_bugs_for_curriculum(curriculum_step)
+
     def reset(self, task_id: str) -> dict:
         """
         Start a fresh episode. Clears all state.
@@ -150,6 +169,228 @@ class DebuggerEnvironment:
             "hint_used": self._observation.hint_used,
         }
 
+    # ── Curriculum Learning ──────────────────────────────────────────────────
+
+    def _load_bugs_for_curriculum(self, step: int) -> list[dict]:
+        """
+        Curriculum schedule:
+        Steps 0-299:   Tier 1 only (easy — off-by-one, wrong operator)
+        Steps 300-599: Tier 1 + Tier 2 (70/30 split)
+        Steps 600+:    Tier 1 + Tier 2 + Tier 3 (40/40/20 split)
+        """
+        def load_tier(tier: int) -> list[dict]:
+            path = f"data/bugs_tier{tier}.jsonl"
+            if not os.path.exists(path):
+                return []
+            bugs = []
+            with open(path) as f:
+                for line in f:
+                    line = line.strip()
+                    if line:
+                        bugs.append(json.loads(line))
+            return bugs
+
+        tier1 = load_tier(1)
+
+        if step < 300:
+            return tier1
+        elif step < 600:
+            tier2 = load_tier(2)
+            n2 = int(len(tier2) * 0.43)  # ~70/30 split
+            return tier1 + tier2[:n2]
+        else:
+            tier2 = load_tier(2)
+            tier3 = load_tier(3)
+            return tier1 + tier2 + tier3
+
+    def advance_curriculum(self, step: int):
+        """Call from training loop at steps 300 and 600."""
+        self.curriculum_step = step
+        self.bugs = self._load_bugs_for_curriculum(step)
+
+    def _active_tiers(self) -> list[int]:
+        if self.curriculum_step < 300:
+            return [1]
+        elif self.curriculum_step < 600:
+            return [1, 2]
+        return [1, 2, 3]
+
+    # ── Curriculum Step / GRPO-Compatible Methods ────────────────────────────
+
+    def reset_curriculum(self) -> dict:
+        """
+        Start a fresh curriculum episode. Selects a random bug from the
+        curriculum-appropriate pool. Returns initial observation dict.
+        """
+        if not self.bugs:
+            raise ValueError("No bugs loaded. Run data/generate_bugs.py first.")
+
+        self.current_bug = random.choice(self.bugs)
+        self.current_episode_trajectory = []
+        self.turn_number = 0
+
+        return {
+            "buggy_code": self.current_bug.get("buggy_code", ""),
+            "error_message": self.current_bug.get("initial_error", "Some tests are failing."),
+            "test_results": {"passed": 0, "failed": 0, "total": len(self.current_bug.get("test_cases", []))},
+            "turn_number": 0,
+            "history": [],
+        }
+
+    def step_curriculum(self, raw_text: str) -> dict:
+        """
+        Process one structured agent response in the curriculum setting.
+        Returns {observation, reward, done, info}.
+        """
+        agent_output = parse_agent_output(raw_text)
+
+        # Run fix against test cases if agent proposes one
+        test_results = {"passed": 0, "failed": 0, "total": 0, "newly_broken": 0}
+        if agent_output.action == "propose_fix" and self.current_bug:
+            test_results = self._run_fix_safely(
+                proposed_code=agent_output.detail,
+                bug=self.current_bug,
+            )
+
+        # Compute reward
+        reward_breakdown = self.reward_calculator.compute_turn_reward(
+            agent_output=agent_output,
+            ground_truth={
+                "bug_function": self.current_bug.get("bug_location", {}).get("function", "") if self.current_bug else "",
+                "bug_line": self.current_bug.get("bug_location", {}).get("line_start", -1) if self.current_bug else -1,
+                "bug_type": self.current_bug.get("bug_type", "") if self.current_bug else "",
+                "canonical_fix_code": self.current_bug.get("original_code", "") if self.current_bug else "",
+            },
+            test_results=test_results,
+            turn_number=self.turn_number,
+        )
+
+        # Record turn in episode trajectory
+        self.current_episode_trajectory.append({
+            "turn": self.turn_number,
+            "agent_output": agent_output,
+            "test_results": test_results,
+            "reward": reward_breakdown,
+        })
+
+        self.turn_number += 1
+
+        # Determine if episode is done
+        solved = reward_breakdown.fix_quality >= 0.35
+        max_turns_reached = self.turn_number >= self.reward_calculator.MAX_TURNS
+        gave_up = agent_output.action == "give_up"
+        done = solved or max_turns_reached or gave_up
+
+        # Log to W&B at episode end
+        if done and WANDB_AVAILABLE:
+            self._log_episode_to_wandb(reward_breakdown, solved)
+
+        return {
+            "observation": {
+                "buggy_code": self.current_bug.get("buggy_code", "") if self.current_bug else "",
+                "error_message": self.current_bug.get("initial_error", "") if self.current_bug else "",
+                "test_results": test_results,
+                "turn_number": self.turn_number,
+                "history": [
+                    {
+                        "turn": t["turn"],
+                        "action": t["agent_output"].action,
+                        "reward": t["reward"].total,
+                    }
+                    for t in self.current_episode_trajectory
+                ],
+            },
+            "reward": reward_breakdown.total,
+            "done": done,
+            "info": {
+                "reward_breakdown": reward_breakdown.__dict__,
+                "turn_number": self.turn_number,
+                "solved": solved,
+                "bug_tier": self.current_bug.get("difficulty", 0) if self.current_bug else 0,
+            },
+        }
+
+    def _run_fix_safely(self, proposed_code: str, bug: dict) -> dict:
+        """Run proposed fix against test cases with timeout. NEVER execute without timeout."""
+        import subprocess
+        import tempfile
+
+        if not proposed_code or not bug.get("test_cases"):
+            return {"passed": 0, "failed": 0, "total": 0, "newly_broken": 0}
+
+        test_cases = bug["test_cases"]
+        func_name = bug.get("function_name", "")
+        passed = 0
+
+        for test in test_cases:
+            inp = test["input"]
+            expected = test["expected_output"]
+
+            if isinstance(inp, (list, tuple)):
+                args_str = ", ".join(repr(x) for x in inp)
+            else:
+                args_str = repr(inp)
+
+            script = f"""
+{proposed_code}
+
+try:
+    result = {func_name}({args_str})
+    expected = {repr(expected)}
+    print("PASS" if result == expected else f"FAIL: got {{result}}, expected {{expected}}")
+except Exception as e:
+    print(f"ERROR: {{type(e).__name__}}: {{e}}")
+"""
+            try:
+                with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
+                    f.write(script)
+                    fname = f.name
+
+                result = subprocess.run(
+                    ["python", fname],
+                    capture_output=True, text=True, timeout=5
+                )
+
+                try:
+                    os.unlink(fname)
+                except Exception:
+                    pass
+
+                if "PASS" in result.stdout:
+                    passed += 1
+            except subprocess.TimeoutExpired:
+                pass  # timeout = failed test
+            except Exception:
+                pass
+
+        failed = len(test_cases) - passed
+        return {
+            "passed": passed,
+            "failed": failed,
+            "total": len(test_cases),
+            "newly_broken": 0,
+        }
+
+    def _log_episode_to_wandb(self, final_reward, solved: bool):
+        """Log episode metrics to W&B. Only called if WANDB_AVAILABLE."""
+        if not WANDB_AVAILABLE:
+            return
+        breakdown = self.reward_calculator.get_reward_breakdown_for_logging(
+            self.current_episode_trajectory
+        )
+        episode_reward = self.reward_calculator.compute_episode_reward(
+            self.current_episode_trajectory
+        )
+
+        wandb.log({
+            "episode/reward_total": episode_reward,
+            "episode/solved": int(solved),
+            "episode/turns_used": self.turn_number,
+            "episode/bug_tier": self.current_bug.get("difficulty", 0) if self.current_bug else 0,
+            "episode/curriculum_step": self.curriculum_step,
+            **breakdown,
+        })
+
     # ── Action Handlers ──────────────────────────────────────────────────────
 
     def _handle_submit_fix(self, action: Action) -> Dict[str, Any]:

env/models.py

@@ -5,8 +5,9 @@ Pydantic v2 data models for structured interaction between the agent
 and the environment, ensuring strict type safety and schema compliance.
 """
 
+import re
 from pydantic import BaseModel
-from typing import List, Dict, Optional
+from typing import List, Dict, Optional, Literal
 
 
 class FixAttempt(BaseModel):
@@ -69,3 +70,65 @@ class Reward(BaseModel):
     cumulative_reward: float      # Sum of all step_rewards this episode
     grader_score: float           # 0.0 during episode. Set ONLY on terminal step (done=True).
     breakdown: Dict[str, float]   # Itemized components
+
+
+# ── STRUCTURED AGENT OUTPUT ────────────────────────────────────────────────
+
+VALID_ACTIONS = {"inspect_lines", "run_tests", "propose_fix", "request_context", "give_up"}
+
+
+class StructuredAgentOutput(BaseModel):
+    observation: str
+    hypothesis: str
+    confidence: Literal["low", "medium", "high"]
+    action: str
+    detail: str
+    valid: bool
+    raw_text: str
+
+
+def parse_agent_output(raw_text: str) -> StructuredAgentOutput:
+    """
+    Parse agent's structured response. Robust to minor formatting variations.
+    Sets valid=False if any required field is missing or action is not in VALID_ACTIONS.
+
+    Expected format:
+        OBSERVATION: [text]
+        HYPOTHESIS: [text]
+        CONFIDENCE: [low|medium|high]
+        ACTION: [inspect_lines|run_tests|propose_fix|request_context|give_up]
+        DETAIL: [text]
+    """
+    def extract_field(text: str, field: str) -> Optional[str]:
+        pattern = rf"(?i){field}\s*:\s*(.*?)(?=\n(?:OBSERVATION|HYPOTHESIS|CONFIDENCE|ACTION|DETAIL)\s*:|$)"
+        match = re.search(pattern, text, re.DOTALL)
+        if match:
+            return match.group(1).strip()
+        return None
+
+    observation = extract_field(raw_text, "OBSERVATION") or ""
+    hypothesis = extract_field(raw_text, "HYPOTHESIS") or ""
+    confidence_raw = (extract_field(raw_text, "CONFIDENCE") or "").lower().strip()
+    action_raw = (extract_field(raw_text, "ACTION") or "").lower().strip()
+    detail = extract_field(raw_text, "DETAIL") or ""
+
+    confidence = confidence_raw if confidence_raw in {"low", "medium", "high"} else "low"
+    action = action_raw if action_raw in VALID_ACTIONS else "invalid"
+
+    valid = all([
+        len(observation) > 5,
+        len(hypothesis) > 10,
+        confidence in {"low", "medium", "high"},
+        action in VALID_ACTIONS,
+        len(detail) > 0,
+    ])
+
+    return StructuredAgentOutput(
+        observation=observation,
+        hypothesis=hypothesis,
+        confidence=confidence,
+        action=action,
+        detail=detail,
+        valid=valid,
+        raw_text=raw_text,
+    )

openenv.yaml

@@ -1,21 +1,61 @@
-name: agentdebugger-env
-version: 1.0.0
+name: AgentDebuggerEnv
+version: "1.0.0"
 description: >
-  A live, iterative debugging environment where AI agents fix broken code
-  by forming hypotheses, submitting fixes, observing test output, and
-  iterating — benchmarking genuine agentic reasoning through a
-  hypothesis-test-fix feedback loop.
+  An OpenEnv-compliant RL training environment where LLM agents learn to debug
+  Python code through structured multi-turn hypothesis-driven reasoning.
+  The agent forms hypotheses, tests them, and refines iteratively over up to 5 turns.
+  Trained via GRPO on Qwen2.5-Coder-7B-Instruct with curriculum learning across
+  3 bug difficulty tiers. Reward design follows Masud et al. (2026) execution-based
+  + process-based taxonomy and Ibrahim et al. (2024) potential-based shaping.
 domain: software_engineering
 tags:
+  - openenv
   - debugging
+  - reinforcement-learning
+  - grpo
+  - curriculum-learning
+  - python
+  - code-reasoning
+  - hypothesis-driven
   - agentic-reasoning
   - code-repair
-  - openenv
   - software-engineering
 observation_type: structured
 action_type: structured
 reward_type: dense
 episode_termination: action_or_step_limit
+observation_space:
+  type: object
+  properties:
+    buggy_code:
+      type: string
+      description: The Python function containing the bug
+    error_message:
+      type: string
+      description: Error output or test failure description seen at episode start
+    test_results:
+      type: object
+      description: Results of running current test suite
+    turn_number:
+      type: integer
+      description: Current turn within episode (0-indexed, max 4)
+    history:
+      type: array
+      description: Previous turns with agent outputs and rewards
+action_space:
+  type: object
+  properties:
+    structured_response:
+      type: string
+      description: >
+        Agent response in required format:
+        OBSERVATION: [text]
+        HYPOTHESIS: [text]
+        CONFIDENCE: [low|medium|high]
+        ACTION: [inspect_lines|run_tests|propose_fix|request_context|give_up]
+        DETAIL: [text]
+reward_range: [-0.5, 1.0]
+max_episode_steps: 5
 inference_script: inference.py
 tasks:
   - id: easy

pyproject.toml

@@ -11,7 +11,7 @@ requires-python = ">=3.10"
 dependencies = [
     "fastapi==0.110.0",
     "uvicorn==0.29.0",
-    "pydantic==2.6.4",
+    "pydantic>=2.9.0",
     "openai==2.7.2",
     "openenv-core>=0.2.0",
     "requests==2.31.0",
@@ -21,5 +21,8 @@ dependencies = [
     "RestrictedPython==7.0"
 ]
 
+[tool.setuptools.packages.find]
+include = ["env*", "server*"]
+
 [project.scripts]
 server = "server.app:main"

server/models.py

@@ -0,0 +1,11 @@
+"""
+server/models.py — Re-exports structured agent types for training scripts.
+All core types live in env/models.py; this module exposes them under the
+`server` namespace so training/train_grpo.py can import without path changes.
+"""
+
+from env.models import (  # noqa: F401
+    StructuredAgentOutput,
+    parse_agent_output,
+    VALID_ACTIONS,
+)

server/reward_calculator.py

@@ -0,0 +1,283 @@
+"""
+DebugRewardCalculator — Multi-component reward system for AgentDebuggerEnv.
+
+Reward taxonomy follows:
+  - Masud et al. (2026) "Reward Engineering for RL in Software Tasks"
+    → Uses their execution-based + process-based + semantic similarity taxonomy
+  - Ibrahim et al. (2024) "Comprehensive Overview of Reward Engineering and Shaping"
+    → Uses potential-based shaping for efficiency component to preserve policy invariance
+
+Design principle: GRPO learns by comparing completions WITHIN a group.
+Relative reward differences matter more than absolute values.
+Therefore: be generous with partial credit so the model gets differentiated signal
+even when nothing fully works.
+"""
+
+import difflib
+import re
+from dataclasses import dataclass
+from typing import Optional
+from server.models import StructuredAgentOutput
+
+
+@dataclass
+class RewardBreakdown:
+    format_compliance: float     # fires every turn — gives early training signal
+    hypothesis_quality: float    # process-based reward (Paper 2 taxonomy)
+    localization: float          # execution-based proxy
+    fix_quality: float           # execution-based reward (primary terminal signal)
+    semantic_similarity: float   # semantic reward (Paper 2 taxonomy)
+    efficiency_potential: float  # potential-based shaping (Paper 1)
+    penalties: float
+    total: float
+
+
+class DebugRewardCalculator:
+    """
+    Reward weights (must sum to 1.0 excluding penalties):
+      format_compliance:    0.10  — fires every turn, drives early curve movement
+      hypothesis_quality:   0.20  — process-based, independent of fix success
+      localization:         0.15  — did agent find the right place?
+      fix_quality:          0.35  — execution-based, primary terminal signal (sparse)
+      semantic_similarity:  0.10  — how close to canonical fix?
+      efficiency_potential: 0.10  — potential-based shaping across turns
+
+    IMPORTANT NOTE ON SPARSITY vs DENSITY:
+    The fix_quality reward (0.35) is sparse — it only fires when tests pass.
+    The format, hypothesis, localization rewards are dense — they fire every turn.
+    This combination is intentional: dense rewards carry gradient signal while the
+    model is still learning to fix bugs; sparse rewards dominate once it gets good.
+    This directly implements Ibrahim et al.'s recommendation to combine reward
+    shaping with terminal rewards to solve the sparse reward problem.
+    """
+
+    MAX_TURNS = 5
+
+    def compute_turn_reward(
+        self,
+        agent_output: StructuredAgentOutput,
+        ground_truth: dict,
+        test_results: dict,
+        turn_number: int,
+    ) -> RewardBreakdown:
+        """
+        Compute reward for a single agent turn.
+
+        Args:
+            agent_output: parsed structured output from the agent
+            ground_truth: {
+                "bug_function": str,   # name of function containing the bug
+                "bug_line": int,       # line number of the bug
+                "bug_type": str,       # category of bug
+                "canonical_fix_code": str,  # the correct minimal fix
+            }
+            test_results: {
+                "passed": int,
+                "failed": int,
+                "total": int,
+                "newly_broken": int,   # tests that passed before but fail after fix
+            }
+            turn_number: 0-indexed turn number within the episode
+
+        Returns:
+            RewardBreakdown with total and all component scores
+        """
+
+        # ── COMPONENT 1: FORMAT COMPLIANCE ────────────────────────────────
+        # This fires EVERY turn. Gives the model early training signal before
+        # it learns to fix bugs. Drives curve movement in first 50-100 steps.
+        if agent_output.valid:
+            format_score = 0.10
+        else:
+            # Partial credit: how many fields were present?
+            fields_present = sum([
+                len(agent_output.observation) > 5,
+                len(agent_output.hypothesis) > 10,
+                agent_output.confidence in {"low", "medium", "high"},
+                agent_output.action in {"inspect_lines", "run_tests", "propose_fix",
+                                        "request_context", "give_up"},
+                len(agent_output.detail) > 0,
+            ])
+            format_score = -0.25 + (fields_present * 0.04)  # -0.25 to -0.05
+
+        # ── COMPONENT 2: HYPOTHESIS QUALITY (Process-based, Paper 2) ──────
+        # Score reasoning quality INDEPENDENTLY from whether the fix works.
+        # A correct diagnosis that leads to a wrong fix still gets rewarded here.
+        # This trains the model to reason carefully even when uncertain.
+        hypothesis_score = 0.0
+        hypothesis = agent_output.hypothesis
+
+        if len(hypothesis.split()) >= 20:
+            hypothesis_score += 0.05   # not a one-liner
+
+        # References specific code elements (backticks, quotes, or operators)
+        if re.search(r'[`\'"<>!=+\-*/]', hypothesis):
+            hypothesis_score += 0.05
+
+        # Mentions line numbers
+        if re.search(r'\bline\s+\d+\b|\b\d+\b', hypothesis):
+            hypothesis_score += 0.05
+
+        # Logically consistent: OBSERVATION and HYPOTHESIS reference same code area
+        obs_words = set(agent_output.observation.lower().split())
+        hyp_words = set(hypothesis.lower().split())
+        overlap = len(obs_words & hyp_words) / max(len(obs_words), 1)
+        if overlap > 0.15:
+            hypothesis_score += 0.05
+
+        # Confidence calibration: rewards correct confidence, penalizes overconfidence
+        # High confidence + correct = bonus, High confidence + wrong = penalty
+        if agent_output.action == "propose_fix":
+            tests_pass = test_results.get("passed", 0) == test_results.get("total", 1)
+            if agent_output.confidence == "high" and tests_pass:
+                hypothesis_score += 0.05   # well-calibrated
+            elif agent_output.confidence == "high" and not tests_pass:
+                hypothesis_score -= 0.05   # overconfident
+            elif agent_output.confidence == "low" and tests_pass:
+                hypothesis_score += 0.02   # humble but correct
+
+        hypothesis_score = max(0.0, min(hypothesis_score, 0.20))
+
+        # ── COMPONENT 3: LOCALIZATION (Execution-based proxy) ─────────────
+        # Did the agent identify WHERE the bug is, independently of fixing it?
+        localization_score = 0.0
+        bug_function = ground_truth.get("bug_function", "").lower()
+        bug_line = str(ground_truth.get("bug_line", -1))
+
+        combined_text = (agent_output.hypothesis + " " + agent_output.detail).lower()
+
+        if bug_function and bug_function in combined_text:
+            localization_score += 0.08
+
+        if bug_line != "-1" and bug_line in agent_output.hypothesis:
+            localization_score += 0.07
+
+        localization_score = min(localization_score, 0.15)
+
+        # ── COMPONENT 4: FIX QUALITY (Execution-based, Paper 2 primary) ───
+        # This is the dominant signal. Sparse but high value.
+        # Paper 1: combine with shaping (components 1-3) to solve sparse problem.
+        total_tests = test_results.get("total", 0)
+        passed_tests = test_results.get("passed", 0)
+        fix_score = 0.0
+
+        if total_tests > 0 and agent_output.action == "propose_fix":
+            pass_rate = passed_tests / total_tests
+            if pass_rate == 1.0:
+                fix_score = 0.35      # full solve — this is what we're training for
+            elif pass_rate >= 0.75:
+                fix_score = 0.20      # most tests pass
+            elif pass_rate >= 0.50:
+                fix_score = 0.12      # more than half pass
+            elif pass_rate > 0.0:
+                fix_score = 0.05      # at least something works
+            # 0.0 if nothing passes — no credit for non-fix actions
+
+        # ── COMPONENT 5: SEMANTIC SIMILARITY (Paper 2 taxonomy) ───────────
+        # How structurally close is the proposed fix to the canonical fix?
+        # Uses difflib — no heavy NLP dependencies needed.
+        semantic_score = 0.0
+        proposed = agent_output.detail
+        canonical = ground_truth.get("canonical_fix_code", "")
+
+        if proposed and canonical and agent_output.action == "propose_fix":
+            similarity = difflib.SequenceMatcher(None, proposed, canonical).ratio()
+            if similarity >= 0.85:
+                semantic_score = 0.10
+            elif similarity >= 0.65:
+                semantic_score = 0.05
+            elif similarity >= 0.40:
+                semantic_score = 0.02
+            # No reward below 0.40 similarity — prevents gaming with partial matches
+
+        # ── COMPONENT 6: EFFICIENCY POTENTIAL (Potential-based, Paper 1) ──
+        # Implements potential-based reward shaping: F(s,a,s') = γΦ(s') - Φ(s)
+        # where Φ(state) = value of remaining turns
+        # This is PROVEN to not change the optimal policy (Ibrahim et al. Theorem 1)
+        # while still accelerating convergence.
+        remaining_turns = self.MAX_TURNS - turn_number
+        efficiency_potential = 0.02 * remaining_turns  # max 0.10 on turn 0
+
+        # ── PENALTIES ─────────────────────────────────────────────────────
+        penalties = 0.0
+
+        # Regression: fix breaks previously-passing tests — severe
+        if test_results.get("newly_broken", 0) > 0:
+            penalties -= 0.20
+
+        # Give up: agent chose to give_up
+        if agent_output.action == "give_up":
+            penalties -= 0.15
+
+        # Invalid action: not one of the 5 valid actions
+        if agent_output.action == "invalid":
+            penalties -= 0.10
+
+        # Invalid format (already captured in format_score, add extra penalty)
+        if not agent_output.valid:
+            penalties -= 0.10
+
+        # ── TOTAL ─────────────────────────────────────────────────────────
+        raw_total = (
+            format_score
+            + hypothesis_score
+            + localization_score
+            + fix_score
+            + semantic_score
+            + efficiency_potential
+            + penalties
+        )
+
+        # Floor at -0.5 to prevent reward death spiral (Ibrahim et al.)
+        total = max(raw_total, -0.5)
+
+        return RewardBreakdown(
+            format_compliance=round(format_score, 4),
+            hypothesis_quality=round(hypothesis_score, 4),
+            localization=round(localization_score, 4),
+            fix_quality=round(fix_score, 4),
+            semantic_similarity=round(semantic_score, 4),
+            efficiency_potential=round(efficiency_potential, 4),
+            penalties=round(penalties, 4),
+            total=round(total, 4),
+        )
+
+    def compute_episode_reward(self, trajectory: list[dict]) -> float:
+        """
+        Aggregate turn rewards across an episode.
+        Uses 0.9 discount factor — later turns worth slightly less.
+        Adds solve bonus if bug was fixed before max turns.
+        """
+        if not trajectory:
+            return 0.0
+
+        total = 0.0
+        discount = 1.0
+
+        for turn in trajectory:
+            total += discount * turn["reward"].total
+            discount *= 0.9
+
+        # Solve bonus: incentivizes actually solving the bug
+        solved = any(t["reward"].fix_quality >= 0.35 for t in trajectory)
+        if solved:
+            total += 0.20
+
+        return round(total, 4)
+
+    def get_reward_breakdown_for_logging(self, trajectory: list[dict]) -> dict:
+        """Returns per-component averages across episode for W&B logging."""
+        if not trajectory:
+            return {}
+
+        components = [
+            "format_compliance", "hypothesis_quality", "localization",
+            "fix_quality", "semantic_similarity", "efficiency_potential", "penalties"
+        ]
+
+        return {
+            f"reward/{c}": round(
+                sum(t["reward"].__dict__[c] for t in trajectory) / len(trajectory), 4
+            )
+            for c in components
+        }

training/train_grpo.py

@@ -0,0 +1,324 @@
+"""
+AgentDebuggerEnv — GRPO Training Script
+Model: Qwen2.5-Coder-7B-Instruct (4-bit quantized via Unsloth)
+Algorithm: GRPO (Group Relative Policy Optimization) via HuggingFace TRL
+GPU: HuggingFace ZeroGPU H200 (free) or paid HF Spaces A10G
+
+Usage:
+  # Test run (no GPU needed, 10 steps):
+  python training/train_grpo.py --test
+
+  # Full training run:
+  python training/train_grpo.py
+
+  # Resume from checkpoint:
+  python training/train_grpo.py --resume ./checkpoints/checkpoint-400
+"""
+
+import os
+import sys
+import json
+import argparse
+import random
+import subprocess
+import tempfile
+import torch
+
+# ── Parse args ────────────────────────────────────────────────────────────────
+parser = argparse.ArgumentParser()
+parser.add_argument("--test", action="store_true", help="Run 10 steps for testing")
+parser.add_argument("--resume", type=str, default=None, help="Path to checkpoint")
+parser.add_argument("--max_steps", type=int, default=1000)
+args = parser.parse_args()
+
+# ── Install dependencies (for Colab/HF Spaces) ───────────────────────────────
+# If running locally with venv, comment these out
+if os.environ.get("COLAB_RELEASE_TAG") or os.environ.get("SPACE_ID"):
+    os.system("pip install -q unsloth trl wandb datasets")
+
+# ── Imports ───────────────────────────────────────────────────────────────────
+import wandb
+from datasets import Dataset
+from unsloth import FastLanguageModel
+from trl import GRPOTrainer, GRPOConfig
+from transformers import TrainerCallback
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from server.reward_calculator import DebugRewardCalculator
+from server.models import parse_agent_output
+
+# ── Configuration ─────────────────────────────────────────────────────────────
+MODEL_NAME = "Qwen/Qwen2.5-Coder-7B-Instruct"
+HF_REPO = "shashaank0707/AgentDebugger-trained"
+MAX_STEPS = 10 if args.test else args.max_steps
+CHECKPOINT_DIR = "./checkpoints"
+
+# W&B — optional but strongly recommended for judging
+WANDB_API_KEY = os.environ.get("WANDB_API_KEY", "")
+if WANDB_API_KEY:
+    wandb.init(
+        project="AgentDebuggerEnv",
+        name=f"grpo-qwen-7b-{'test' if args.test else 'full'}",
+        config={
+            "model": MODEL_NAME,
+            "algorithm": "GRPO",
+            "curriculum": "tier1->tier2->tier3",
+            "max_steps": MAX_STEPS,
+            "reward_components": ["format", "hypothesis", "localization", "fix", "semantic", "efficiency"],
+            "paper_citations": ["Masud et al. 2026", "Ibrahim et al. 2024"],
+        }
+    )
+
+# ── System prompt ─────────────────────────────────────────────────────────────
+SYSTEM_PROMPT = """You are an expert Python debugger. You reason through bugs systematically.
+
+You MUST respond in EXACTLY this format — no exceptions, no extra text:
+
+OBSERVATION: [Specific observations about the code and error. Reference exact line numbers.]
+HYPOTHESIS: [Your theory about the root cause. Must be at least 2 sentences. Reference specific variable names, operators, or logic.]
+CONFIDENCE: [low | medium | high]
+ACTION: [One of: inspect_lines | run_tests | propose_fix | request_context | give_up]
+DETAIL: [For propose_fix: the complete corrected function code. For inspect_lines: line numbers. For others: specific details.]
+
+Rules:
+- Never omit any field
+- HYPOTHESIS must explain WHY the bug causes the observed failure
+- If proposing a fix, DETAIL must contain the complete function, not just the changed line
+- Give up only if you have exhausted all reasonable hypotheses"""
+
+# ── Load bugs ─────────────────────────────────────────────────────────────────
+def load_bugs(tier: int) -> list[dict]:
+    path = f"data/bugs_tier{tier}.jsonl"
+    if not os.path.exists(path):
+        print(f"WARNING: {path} not found. Run data/generate_bugs.py first.")
+        return []
+    with open(path) as f:
+        return [json.loads(line) for line in f if line.strip()]
+
+def get_bugs_for_step(step: int) -> list[dict]:
+    tier1 = load_bugs(1)
+    if step < 300:
+        return tier1
+    elif step < 600:
+        tier2 = load_bugs(2)
+        return tier1 + tier2[:int(len(tier2) * 0.43)]
+    return tier1 + load_bugs(2) + load_bugs(3)
+
+def bug_to_prompt(bug: dict) -> str:
+    return (
+        f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
+        f"<|im_start|>user\n"
+        f"Debug this Python function:\n\n```python\n{bug['buggy_code']}\n```\n\n"
+        f"Initial failure: {bug.get('initial_error', 'Some tests are failing.')}\n"
+        f"<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+
+# ── Load model ────────────────────────────────────────────────────────────────
+print(f"Loading {MODEL_NAME}...")
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=MODEL_NAME,
+    max_seq_length=4096,
+    load_in_4bit=True,
+    dtype=None,
+)
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=16,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
+                    "gate_proj", "up_proj", "down_proj"],
+    lora_alpha=16,
+    lora_dropout=0,
+    bias="none",
+    use_gradient_checkpointing="unsloth",
+    random_state=42,
+)
+print(f"Trainable params: {model.num_parameters(only_trainable=True):,}")
+
+# ── Reward function ───────────────────────────────────────────────────────────
+calculator = DebugRewardCalculator()
+
+def reward_fn(completions: list[str], prompts: list[str], **kwargs) -> list[float]:
+    """
+    GRPO reward function. Called on groups of completions for the same prompt.
+    GRPO learns from RELATIVE differences within each group.
+    """
+    rewards = []
+    bugs = kwargs.get("bug_metadata", [{}] * len(completions))
+
+    for completion, bug in zip(completions, bugs):
+        try:
+            agent_output = parse_agent_output(completion)
+
+            # Run fix if agent proposes one
+            test_results = {"passed": 0, "failed": 0, "total": 0, "newly_broken": 0}
+            if agent_output.action == "propose_fix" and bug:
+                test_results = _run_fix(agent_output.detail, bug)
+
+            breakdown = calculator.compute_turn_reward(
+                agent_output=agent_output,
+                ground_truth={
+                    "bug_function": bug.get("bug_location", {}).get("function", ""),
+                    "bug_line": bug.get("bug_location", {}).get("line_start", -1),
+                    "bug_type": bug.get("bug_type", ""),
+                    "canonical_fix_code": bug.get("original_code", ""),
+                },
+                test_results=test_results,
+                turn_number=0,
+            )
+
+            if WANDB_API_KEY:
+                wandb.log({k: v for k, v in breakdown.__dict__.items()})
+
+            rewards.append(breakdown.total)
+
+        except Exception as e:
+            print(f"Reward error: {e}")
+            rewards.append(-0.3)
+
+    return rewards
+
+def _run_fix(proposed_code: str, bug: dict) -> dict:
+    """Safely run proposed fix with subprocess timeout."""
+    test_cases = bug.get("test_cases", [])
+    func_name = bug.get("function_name", "")
+    if not proposed_code or not test_cases or not func_name:
+        return {"passed": 0, "failed": 0, "total": len(test_cases), "newly_broken": 0}
+
+    passed = 0
+    for test in test_cases:
+        inp = test["input"]
+        args_str = ", ".join(repr(x) for x in inp) if isinstance(inp, (list, tuple)) else repr(inp)
+        script = (
+            f"{proposed_code}\n"
+            f"try:\n"
+            f"    r={func_name}({args_str})\n"
+            f"    print('PASS' if r=={repr(test['expected_output'])} else 'FAIL')\n"
+            f"except Exception as e:\n"
+            f"    print(f'ERROR: {{e}}')\n"
+        )
+        try:
+            with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
+                f.write(script)
+                fname = f.name
+            r = subprocess.run(["python", fname], capture_output=True, text=True, timeout=5)
+            os.unlink(fname)
+            if "PASS" in r.stdout:
+                passed += 1
+        except Exception:
+            pass
+
+    return {"passed": passed, "failed": len(test_cases) - passed, "total": len(test_cases), "newly_broken": 0}
+
+# ── Baseline evaluation (run BEFORE training) ─────────────────────────────────
+def run_baseline(n: int = 20) -> dict:
+    print("\nRunning baseline evaluation on UNTRAINED model...")
+    FastLanguageModel.for_inference(model)
+    bugs = load_bugs(1)[:n]
+    rewards = []
+    solved = 0
+    for bug in bugs:
+        prompt = bug_to_prompt(bug)
+        inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+        with torch.no_grad():
+            out = model.generate(**inputs, max_new_tokens=400, temperature=0.1, do_sample=False)
+        completion = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+        r = reward_fn([completion], [prompt], bug_metadata=[bug])
+        rewards.append(r[0])
+        if r[0] > 0.30:
+            solved += 1
+
+    result = {"solve_rate": solved / max(len(bugs), 1), "avg_reward": sum(rewards) / max(len(rewards), 1), "rewards": rewards}
+    with open("baseline_results.json", "w") as f:
+        json.dump(result, f)
+    print(f"Baseline: solve_rate={result['solve_rate']:.1%}, avg_reward={result['avg_reward']:.3f}")
+    if WANDB_API_KEY:
+        wandb.log({"baseline/solve_rate": result["solve_rate"], "baseline/avg_reward": result["avg_reward"]})
+    return result
+
+baseline = run_baseline()
+FastLanguageModel.for_training(model)
+
+# ── Build initial dataset ─────────────────────────────────────────────────────
+def make_dataset(step: int) -> Dataset:
+    bugs = get_bugs_for_step(step)
+    return Dataset.from_list([{"prompt": bug_to_prompt(b), "bug_metadata": b} for b in bugs])
+
+# ── Training config ───────────────────────────────────────────────────────────
+config = GRPOConfig(
+    output_dir=CHECKPOINT_DIR,
+    max_steps=MAX_STEPS,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    learning_rate=1e-5,
+    lr_scheduler_type="cosine",
+    warmup_steps=20 if args.test else 50,
+    num_generations=4,
+    max_new_tokens=400,
+    temperature=0.8,
+    logging_steps=5 if args.test else 10,
+    save_steps=50 if args.test else 100,
+    report_to="wandb" if WANDB_API_KEY else "none",
+)
+
+trainer = GRPOTrainer(
+    model=model,
+    args=config,
+    train_dataset=make_dataset(0),
+    reward_funcs=reward_fn,
+    tokenizer=tokenizer,
+)
+
+# ── Curriculum callback ───────────────────────────────────────────────────────
+class CurriculumCallback(TrainerCallback):
+    def on_step_end(self, args, state, control, **kwargs):
+        step = state.global_step
+        if step in [300, 600]:
+            trainer.train_dataset = make_dataset(step)
+            print(f"\nCurriculum advanced at step {step}!")
+            if WANDB_API_KEY:
+                wandb.log({"curriculum/step": step})
+
+trainer.add_callback(CurriculumCallback())
+
+# ── Train ─────────────────────────────────────────────────────────────────────
+print(f"\nStarting GRPO training. Max steps: {MAX_STEPS}")
+print(f"Baseline solve rate: {baseline['solve_rate']:.1%} — target: >60% after training")
+trainer.train(resume_from_checkpoint=args.resume)
+
+# ── Post-training evaluation ──────────────────────────────────────────────────
+FastLanguageModel.for_inference(model)
+bugs = load_bugs(1)[:20]
+post_rewards = []
+post_solved = 0
+for bug in bugs:
+    prompt = bug_to_prompt(bug)
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    with torch.no_grad():
+        out = model.generate(**inputs, max_new_tokens=400, temperature=0.1, do_sample=False)
+    completion = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+    r = reward_fn([completion], [prompt], bug_metadata=[bug])
+    post_rewards.append(r[0])
+    if r[0] > 0.30:
+        post_solved += 1
+
+post_solve_rate = post_solved / max(len(bugs), 1)
+print(f"\n{'='*60}")
+print(f"RESULTS:")
+print(f"Before training: {baseline['solve_rate']:.1%} solve rate")
+print(f"After training:  {post_solve_rate:.1%} solve rate")
+print(f"Improvement:     +{post_solve_rate - baseline['solve_rate']:.1%}")
+print(f"{'='*60}")
+
+if WANDB_API_KEY:
+    wandb.log({"final/solve_rate": post_solve_rate, "final/improvement": post_solve_rate - baseline["solve_rate"]})
+    wandb.finish()
+
+# ── Save and push ─────────────────────────────────────────────────────────────
+model.save_pretrained("./final_model")
+tokenizer.save_pretrained("./final_model")
+HF_TOKEN = os.environ.get("HF_TOKEN")
+if HF_TOKEN and not args.test:
+    model.push_to_hub(HF_REPO, token=HF_TOKEN)
+    tokenizer.push_to_hub(HF_REPO, token=HF_TOKEN)
+    print(f"Pushed to https://huggingface.co/{HF_REPO}")

uv.lock

@@ -25,11 +25,11 @@ requires-dist = [
     { name = "httpx", specifier = "==0.27.0" },
     { name = "openai", specifier = "==2.7.2" },
     { name = "openenv-core", specifier = ">=0.2.0" },
-    { name = "pydantic", specifier = "==2.6.4" },
+    { name = "pydantic", specifier = ">=2.9.0" },
     { name = "pytest", specifier = "==8.1.0" },
     { name = "python-dotenv", specifier = "==1.0.1" },
     { name = "requests", specifier = "==2.31.0" },
-    { name = "restrictedpython", specifier = "==7.4" },
+    { name = "restrictedpython", specifier = "==7.0" },
     { name = "uvicorn", specifier = "==0.29.0" },
 ]
 
@@ -541,73 +541,133 @@ wheels = [
 
 [[package]]
 name = "pydantic"
-version = "2.6.4"
+version = "2.13.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "annotated-types" },
     { name = "pydantic-core" },
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