बेहतर कोडिंग एजेंट प्रशिक्षित करने के लिए प्रोसेस रिवॉर्ड डेटा कैसे जुटाएँ

annotation_schemes:
  - annotation_type: process_reward
    name: prm_first_error
    mode: "first_error"
    description: >
      Review the agent's steps from top to bottom. Click on the
      first step where the agent makes a mistake. All steps before
      your selection will be marked correct; all steps after
      (including the selected step) will be marked incorrect.

annotation_schemes:
  - annotation_type: process_reward
    name: prm_per_step
    mode: "per_step"

{
  "id": "trace_001",
  "issue_description": "Fix the TypeError in parse_config() when config file is empty",
  "repo": "myorg/myproject",
  "base_commit": "a1b2c3d",
  "structured_turns": [
    {
      "step_idx": 0,
      "type": "thinking",
      "content": "I need to understand what parse_config does and where the TypeError occurs. Let me read the file first."
    },
    {
      "step_idx": 1,
      "type": "file_read",
      "path": "src/config.py",
      "content": "def parse_config(path):\n    with open(path) as f:\n        data = json.load(f)\n    return data['settings']",
      "start_line": 1,
      "end_line": 4
    },
    {
      "step_idx": 2,
      "type": "thinking",
      "content": "The bug is on line 4. When the file is empty, json.load returns None, and None['settings'] raises TypeError. I should add a check."
    },
    {
      "step_idx": 3,
      "type": "file_edit",
      "path": "src/config.py",
      "diff": "--- a/src/config.py\n+++ b/src/config.py\n@@ -1,4 +1,6 @@\n def parse_config(path):\n     with open(path) as f:\n         data = json.load(f)\n+    if data is None:\n+        return {}\n     return data['settings']"
    },
    {
      "step_idx": 4,
      "type": "bash_command",
      "command": "python -m pytest tests/test_config.py -v",
      "output": "tests/test_config.py::test_parse_config_empty PASSED\ntests/test_config.py::test_parse_config_valid PASSED\n\n2 passed in 0.12s",
      "exit_code": 0
    }
  ]
}

# Convert Claude Code traces
potato convert-traces \
  --format claude_code \
  --input ./raw_traces/ \
  --output ./data/traces.jsonl
 
# Convert SWE-Agent trajectories
potato convert-traces \
  --format swe_agent \
  --input ./swe_agent_output/ \
  --output ./data/traces.jsonl

# config.yaml
project_name: "PRM Data Collection - SWE-bench Traces"
port: 8000
 
data:
  source: "local"
  input_path: "./data/traces.jsonl"
  data_format: "coding_trace"
 
coding_agent:
  display:
    diff_style: "unified"
    context_lines: 3
    syntax_highlighting: true
    terminal_theme: "dark"
    file_tree:
      enabled: true
      position: "left"
    collapsible:
      auto_collapse_thinking: true
      auto_collapse_long_output: true
      long_output_threshold: 50
 
annotation_schemes:
  - annotation_type: process_reward
    name: step_reward
    mode: "first_error"
    description: >
      Review the agent's trajectory step by step. Click the first
      step where the agent makes an error. If the entire trajectory
      is correct, click "All Correct."
 
  - annotation_type: radio
    name: outcome
    labels:
      - value: "resolved"
        text: "Fully Resolved"
      - value: "partial"
        text: "Partially Resolved"
      - value: "not_resolved"
        text: "Not Resolved"
 
  - annotation_type: text
    name: error_description
    description: "If incorrect, briefly describe the error"
    placeholder: "e.g., Agent edited the wrong file..."
 
output:
  path: "./output/"
  format: "jsonl"
 
quality_control:
  inter_annotator_agreement: true
  overlap_percentage: 15
  minimum_time_per_instance: 20
 
annotators:
  - username: "reviewer1"
  - username: "reviewer2"
  - username: "reviewer3"

# Start the annotation server
potato start config.yaml -p 8000
 
# Or run in the background
nohup potato start config.yaml -p 8000 > potato.log 2>&1 &

# Check annotation progress
potato status config.yaml
 
# View inter-annotator agreement
potato agreement config.yaml --metric krippendorff_alpha

potato export \
  --format prm \
  --project ./output/ \
  --output ./training_data/prm_labels.jsonl

{
  "trace_id": "trace_001",
  "issue_description": "Fix the TypeError in parse_config() when config file is empty",
  "total_steps": 5,
  "first_error_step": null,
  "all_correct": true,
  "steps": [
    {"step_idx": 0, "type": "thinking", "label": "correct", "reward": 1.0},
    {"step_idx": 1, "type": "file_read", "label": "correct", "reward": 1.0},
    {"step_idx": 2, "type": "thinking", "label": "correct", "reward": 1.0},
    {"step_idx": 3, "type": "file_edit", "label": "correct", "reward": 1.0},
    {"step_idx": 4, "type": "bash_command", "label": "correct", "reward": 1.0}
  ]
}

potato export \
  --format preference_pairs \
  --project ./output/ \
  --output ./training_data/preferences.jsonl \
  --pair_by "issue_id"

{
  "prompt": "Fix the TypeError in parse_config() when config file is empty",
  "chosen_trace_id": "trace_001",
  "rejected_trace_id": "trace_002",
  "chosen_first_error": null,
  "rejected_first_error": 3,
  "chosen_steps": 5,
  "rejected_steps": 7,
  "margin": 0.8
}

potato export \
  --format swe_bench \
  --project ./output/ \
  --output ./training_data/swe_bench_results.json

import json
from collections import defaultdict
 
# Load PRM annotations
with open("training_data/prm_labels.jsonl") as f:
    traces = [json.loads(line) for line in f]
 
# Compute accuracy by step type
type_stats = defaultdict(lambda: {"correct": 0, "total": 0})
 
for trace in traces:
    for step in trace["steps"]:
        step_type = step["type"]
        type_stats[step_type]["total"] += 1
        if step["label"] == "correct":
            type_stats[step_type]["correct"] += 1
 
print("Step-Level Accuracy by Type:")
print("-" * 45)
for step_type, stats in sorted(type_stats.items()):
    acc = stats["correct"] / stats["total"] * 100
    print(f"  {step_type:<20} {acc:5.1f}%  ({stats['correct']}/{stats['total']})")

import json
from collections import Counter
 
with open("training_data/prm_labels.jsonl") as f:
    traces = [json.loads(line) for line in f]
 
# Analyze where errors first occur
error_positions = []
error_types_at_first_error = Counter()
 
for trace in traces:
    if trace["first_error_step"] is not None:
        pos = trace["first_error_step"]
        total = trace["total_steps"]
        # Normalize position to 0-1 range
        error_positions.append(pos / total)
        # Track what type of step caused the first error
        error_step = trace["steps"][pos]
        error_types_at_first_error[error_step["type"]] += 1
 
if error_positions:
    avg_pos = sum(error_positions) / len(error_positions)
    print(f"Average first-error position: {avg_pos:.2f} (0=start, 1=end)")
    print(f"Traces with errors: {len(error_positions)}/{len(traces)}")
    print()
    print("Most common step types at first error:")
    for step_type, count in error_types_at_first_error.most_common(5):
        print(f"  {step_type}: {count}")

import json
import numpy as np
from sklearn.metrics import cohen_kappa_score
 
def load_annotations(annotator_file):
    """Load annotations from a single annotator's output file."""
    with open(annotator_file) as f:
        data = {item["trace_id"]: item for item in
                (json.loads(line) for line in f)}
    return data
 
ann1 = load_annotations("output/reviewer1/annotations.jsonl")
ann2 = load_annotations("output/reviewer2/annotations.jsonl")
 
# Find overlapping traces
overlap_ids = set(ann1.keys()) & set(ann2.keys())
print(f"Overlapping traces: {len(overlap_ids)}")
 
# Compare first-error step labels
labels1 = []
labels2 = []
for trace_id in overlap_ids:
    fe1 = ann1[trace_id].get("first_error_step", -1)
    fe2 = ann2[trace_id].get("first_error_step", -1)
    # Bin into: all_correct, early_error (first half), late_error (second half)
    total = ann1[trace_id]["total_steps"]
    for fe, labels in [(fe1, labels1), (fe2, labels2)]:
        if fe is None or fe == -1:
            labels.append("all_correct")
        elif fe < total / 2:
            labels.append("early_error")
        else:
            labels.append("late_error")
 
kappa = cohen_kappa_score(labels1, labels2)
print(f"Cohen's kappa (binned first-error): {kappa:.3f}")