如何为训练更好的编程智能体收集过程奖励数据

annotation_schemes:
  - annotation_type: process_reward
    name: step_rewards
    description: "Click the first step where the agent makes a mistake. Steps after it are flagged automatically."
    mode: first_error
    steps_key: structured_turns
    step_text_key: content

annotation_schemes:
  - annotation_type: process_reward
    name: step_rewards
    description: "Rate each step correct, neutral, or incorrect"
    mode: per_step
    steps_key: structured_turns
    step_text_key: content
    allow_neutral: true
    inline_with_trace: true

{
  "id": "trace_001",
  "issue_description": "Fix the TypeError in parse_config() when the config file is empty",
  "structured_turns": [
    {"step_idx": 0, "type": "thinking", "content": "I need to find where the TypeError occurs. Let me read the file."},
    {"step_idx": 1, "type": "file_read", "content": "def parse_config(path):\n    with open(path) as f:\n        data = json.load(f)\n    return data['settings']"},
    {"step_idx": 2, "type": "thinking", "content": "When the file is empty json.load returns None, so None['settings'] raises. Add a check."},
    {"step_idx": 3, "type": "file_edit", "content": "+    if data is None:\n+        return {}"},
    {"step_idx": 4, "type": "bash_command", "content": "python -m pytest tests/test_config.py -v  ->  2 passed"}
  ]
}

python -m potato.trace_converter \
  --input claude_code_sessions.json \
  --input-format claude_code \
  --output data/traces.jsonl

annotation_task_name: "PRM Data Collection"
task_dir: "."
 
data_files:
  - "data/traces.jsonl"
 
item_properties:
  id_key: id
  text_key: issue_description
 
instance_display:
  fields:
    - key: issue_description
      type: text
      label: "Issue"
 
    - key: structured_turns
      type: coding_trace
      label: "Agent trajectory"
      display_options:
        diff_view: unified
        show_file_tree: true
        show_reasoning: true
        terminal_theme: dark
        collapse_long_outputs: true
        max_output_lines: 50
 
annotation_schemes:
  - annotation_type: process_reward
    name: step_rewards
    description: "Click the first step where the agent makes an error"
    mode: first_error
    steps_key: structured_turns
    step_text_key: content
 
  - annotation_type: radio
    name: outcome
    description: "Did the agent resolve the issue?"
    labels:
      - name: resolved
        text: "Fully resolved"
      - name: partial
        text: "Partially resolved"
      - name: not_resolved
        text: "Not resolved"
 
  - annotation_type: text
    name: error_description
    description: "If the trajectory went wrong, describe the error briefly"
    rows: 3
    placeholder: "e.g. edited the wrong file"
    label_requirement:
      required: false
 
num_annotators_per_item: 2
 
attention_checks:
  enabled: true
  items_file: "data/attention_checks.json"
  frequency: 20
  min_response_time: 20.0
  failure_handling:
    warn_threshold: 2
    block_threshold: 5
 
user_config:
  allow_all_users: false
  users:
    - reviewer1@example.com
    - reviewer2@example.com
    - reviewer3@example.com
 
output_annotation_dir: "annotation_output/"
export_annotation_format: jsonl

potato start config.yaml -p 8000

python -m potato.export -c config.yaml -f coding_eval -o ./export/

{
  "step_rewards": {
    "mode": "first_error",
    "steps": [
      {"reward": 1, "source": "human", "verified": true},
      {"reward": 1, "source": "human", "verified": true},
      {"reward": -1, "source": "human", "verified": true},
      {"reward": -1, "source": "human", "verified": true}
    ]
  }
}

import json
from pathlib import Path
 
SCHEME = "step_rewards"
 
examples = []
for f in Path("export/").rglob("*.jsonl"):
    with open(f) as fh:
        for line in fh:
            rec = json.loads(line)
            payload = (rec.get("labels", rec) or {}).get(SCHEME)
            if not payload:
                continue
 
            steps = payload.get("steps", [])
            # Drop traces with unjudged steps rather than guessing at them.
            if any(s.get("reward") is None for s in steps):
                continue
 
            item = items_by_id[rec["instance_id"]]      # your own data file
            examples.append({
                "trace_id": rec["instance_id"],
                "prompt": item["issue_description"],
                "steps": [
                    {"content": src["content"], "label": s["reward"]}
                    for src, s in zip(item["structured_turns"], steps)
                ],
            })
 
with open("prm_labels.jsonl", "w") as out:
    for ex in examples:
        out.write(json.dumps(ex) + "\n")
 
print(f"Wrote {len(examples)} traces")

import json
from pathlib import Path
 
SCHEME = "step_rewards"
 
traces = []
for f in Path("export/").rglob("*.jsonl"):
    with open(f) as fh:
        for line in fh:
            payload = (json.loads(line).get("labels", {}) or {}).get(SCHEME)
            if payload:
                traces.append(payload)
 
positions = []
all_correct = 0
for t in traces:
    steps = t.get("steps", [])
    first = next((i for i, s in enumerate(steps) if s.get("reward") == -1), None)
    if first is None:
        all_correct += 1
    elif len(steps) > 1:
        positions.append(first / (len(steps) - 1))
 
print(f"Traces annotated: {len(traces)}")
print(f"No errors: {all_correct} ({all_correct / len(traces):.1%})")
if positions:
    print(f"Average first-error position: {sum(positions) / len(positions):.2f} (0=start, 1=end)")

 
def bin_position(steps):
    first = next((i for i, s in enumerate(steps) if s.get("reward") == -1), None)
    if first is None:
        return "all_correct"
    return "early_error" if first < len(steps) / 2 else "late_error"