fix: validate tool args are dict in ToolEnv.env_response

tests/test_tool_env.py

@@ -427,3 +427,124 @@ def invalid_type_tool() -> list:
 
         result = await env.call_tool("invalid_type_tool", {}, "call_0")
         assert result["content"] == "[{'type': 'audio', 'data': 'base64data'}]"
+
+
+class TestToolEnvNonDictArgs:
+    """Tests for ToolEnv handling of non-dict tool arguments (issue #562).
+
+    When a model produces double-encoded JSON, json.loads succeeds but returns
+    a str instead of a dict. ToolEnv should handle this gracefully rather than
+    crashing the RL training run.
+    """
+
+    @pytest.mark.asyncio
+    async def test_tool_env_non_dict_args_stops_when_configured(
+        self, mock_client, sample_chat_dataset, make_input
+    ):
+        """Test that ToolEnv stops rollout when tool args are not a dict and ValueError is in stop_errors."""
+
+        class StrictToolEnv(vf.ToolEnv):
+            def __init__(self, **kwargs):
+                super().__init__(
+                    tools=[square_tool], stop_errors=[ValueError], **kwargs
+                )
+
+        env = StrictToolEnv(
+            client=mock_client,
+            model="test-model",
+            dataset=sample_chat_dataset,
+            parser=vf.Parser(),
+            rubric=vf.Rubric(),
+        )
+
+        # Create a tool call with double-encoded JSON arguments (json.loads
+        # succeeds but returns str instead of dict)
+        from openai.types.chat.chat_completion_message_tool_call import (
+            ChatCompletionMessageToolCall,
+            Function,
+        )
+
+        double_encoded_args = json.dumps(json.dumps({"x": 4}))
+        tool_call = ChatCompletionMessageToolCall(
+            id="call_0",
+            type="function",
+            function=Function(
+                name="square_tool",
+                arguments=double_encoded_args,
+            ),
+        )
+
+        mock_client.add_response(
+            messages=[{"role": "user", "content": "Square 4"}],
+            response="Using tool",
+            tool_calls=[tool_call],
+        )
+
+        state = await env.rollout(
+            input=make_input(
+                prompt=[{"role": "user", "content": "Square 4"}], answer="", task=""
+            ),
+            client=mock_client,
+            model="test-model",
+        )
+
+        assert state.get("error") is not None
+        assert isinstance(state["error"], vf.ToolParseError)
+        assert isinstance(state["error"], vf.ToolError)
+        assert state["is_completed"] is True
+        assert state["stop_condition"] == "has_error"
+
+    @pytest.mark.asyncio
+    async def test_tool_env_non_dict_args_graceful_fallback(
+        self, mock_client, sample_chat_dataset, make_input
+    ):
+        """Test that ToolEnv gracefully returns error message when tool args are
+        not a dict and no stop_errors are configured."""
+
+        env = vf.ToolEnv(
+            tools=[square_tool],
+            client=mock_client,
+            model="test-model",
+            dataset=sample_chat_dataset,
+            parser=vf.Parser(),
+            rubric=vf.Rubric(),
+        )
+
+        from openai.types.chat.chat_completion_message_tool_call import (
+            ChatCompletionMessageToolCall,
+            Function,
+        )
+
+        # Simulate double-encoded JSON: json.loads returns a str, not a dict
+        double_encoded_args = json.dumps(json.dumps({"x": 4}))
+        tool_call_bad = ChatCompletionMessageToolCall(
+            id="call_0",
+            type="function",
+            function=Function(
+                name="square_tool",
+                arguments=double_encoded_args,
+            ),
+        )
+
+        mock_client.add_response(
+            messages=[{"role": "user", "content": "Square 4"}],
+            response="Using tool",
+            tool_calls=[tool_call_bad],
+        )
+
+        # Second response (after error feedback) completes normally
+        mock_client.set_default_response("Done")
+
+        state = await env.rollout(
+            input=make_input(
+                prompt=[{"role": "user", "content": "Square 4"}], answer="", task=""
+            ),
+            client=mock_client,
+            model="test-model",
+        )
+
+        # Should NOT crash - error is returned as a tool message, training continues
+        completion = state["completion"]
+        tool_messages = [m for m in completion if m.get("role") == "tool"]
+        assert len(tool_messages) >= 1
+        assert "Expected tool arguments to be a dict" in tool_messages[0]["content"]

verifiers/envs/tool_env.py

@@ -152,7 +152,12 @@ async def env_response(
             tool_call_id: str = tool_call.id
             try:
                 tool_name: str = tool_call.name
-                tool_args: dict = json.loads(tool_call.arguments)
+                parsed_args = json.loads(tool_call.arguments)
+                if not isinstance(parsed_args, dict):
+                    raise ValueError(
+                        f"Expected tool arguments to be a dict, got {type(parsed_args).__name__}: {parsed_args}"
+                    )
+                tool_args: dict = parsed_args
             except Exception as e:
                 if self._should_stop_for_error(e):
                     raise vf.ToolParseError from e