| import json |
| import numpy as np |
| import matplotlib.pyplot as plt |
|
|
| def load_graded(path): |
| with open(path) as f: |
| data = json.load(f) |
| return [item["graded_list"] for item in data] |
|
|
| def bootstrap_pass_at_k(graded_lists, k, n_bootstrap=10000, rng=None): |
| if rng is None: |
| rng = np.random.default_rng(42) |
| problem_scores = [] |
| for outcomes in graded_lists: |
| outcomes_arr = np.array(outcomes, dtype=bool) |
| n = len(outcomes_arr) |
| samples = rng.integers(0, n, size=(n_bootstrap, k)) |
| any_pass = outcomes_arr[samples].any(axis=1) |
| problem_scores.append(any_pass.mean()) |
| return np.mean(problem_scores) |
|
|
| with open("output/base_model_evals.json") as f: |
| base_evals = json.load(f) |
| one_shot = base_evals["results"]["generic_base_one_shot"] |
|
|
| maxrl_steps = [100, 200, 300, 400] |
| tailrl_steps = [100, 200, 300, 400] |
| k_values = [1, 2, 4, 8, 16] |
| left_k_values = [1, 16] |
|
|
| maxrl_data = {} |
| for step in maxrl_steps: |
| path = f"output/maxrl_binary_{step}/Scenario.codegeneration_16_0.6_eval_all.json" |
| graded = load_graded(path) |
| rng = np.random.default_rng(42) |
| maxrl_data[step] = {k: bootstrap_pass_at_k(graded, k, rng=rng) for k in k_values} |
|
|
| tailrl_data = {} |
| for step in tailrl_steps: |
| path = f"output/tailrl_cont_{step}/Scenario.codegeneration_16_0.6_eval_all.json" |
| graded = load_graded(path) |
| rng = np.random.default_rng(42) |
| tailrl_data[step] = {k: bootstrap_pass_at_k(graded, k, rng=rng) for k in k_values} |
|
|
| results_json = { |
| "base_one_shot": {f"pass@{k}": one_shot[f"pass@{k}"] for k in k_values}, |
| "maxrl_binary": {str(step): {f"pass@{k}": float(v) for k, v in ks.items()} for step, ks in maxrl_data.items()}, |
| "tailrl_cont": {str(step): {f"pass@{k}": float(v) for k, v in ks.items()} for step, ks in tailrl_data.items()}, |
| } |
| results_path = "output/pass_at_k_results.json" |
| with open(results_path, "w") as f: |
| json.dump(results_json, f, indent=2) |
| print(f"Saved {results_path}") |
|
|
| fig, (ax_left, ax_right) = plt.subplots(1, 2, figsize=(14, 5)) |
|
|
| maxrl_colors = {'p@1': 'steelblue', 'p@16': 'navy'} |
| tailrl_colors = {'p@1': 'darkorange', 'p@16': 'saddlebrown'} |
| markers = {1: 'o', 16: 's'} |
|
|
| |
| for k in left_k_values: |
| vals_maxrl = [maxrl_data[s][k] for s in maxrl_steps] |
| vals_tailrl = [tailrl_data[s][k] for s in tailrl_steps] |
| ax_left.plot(maxrl_steps, vals_maxrl, marker=markers[k], color=maxrl_colors[f'p@{k}'], |
| linewidth=2, markersize=7, label=f"maxrl p@{k}") |
| ax_left.plot(tailrl_steps, vals_tailrl, marker=markers[k], color=tailrl_colors[f'p@{k}'], |
| linewidth=2, markersize=7, linestyle='--', label=f"tailrl p@{k}") |
|
|
| for k, ls in zip(left_k_values, ['-', '--']): |
| ax_left.axhline(one_shot[f"pass@{k}"], color='gray', linewidth=1.5, |
| linestyle=ls, label=f"base one-shot p@{k}") |
|
|
| ax_left.set_xlabel("Training Steps", fontsize=12) |
| ax_left.set_ylabel("pass@k", fontsize=12) |
| ax_left.set_title("Performance vs Training Steps", fontsize=13) |
| ax_left.set_xticks(sorted(set(maxrl_steps + tailrl_steps))) |
| ax_left.legend(fontsize=10, loc="lower right") |
| ax_left.grid(True, alpha=0.3) |
|
|
| |
| last_maxrl = [maxrl_data[400][k] for k in k_values] |
| last_tailrl = [tailrl_data[400][k] for k in k_values] |
|
|
| base_one_shot_vals = [one_shot[f"pass@{k}"] for k in k_values] |
| ax_right.plot(k_values, base_one_shot_vals, marker='^', color='gray', |
| linewidth=2, markersize=8, linestyle='--', label="base one-shot") |
| for k, v in zip(k_values, base_one_shot_vals): |
| ax_right.annotate(f"{v:.3f}", (k, v), textcoords="offset points", xytext=(5, 6), |
| fontsize=8, color='gray') |
|
|
| ax_right.plot(k_values, last_maxrl, marker='o', color='steelblue', |
| linewidth=2, markersize=8, label="maxrl_binary_400") |
| ax_right.plot(k_values, last_tailrl, marker='s', color='darkorange', |
| linewidth=2, markersize=8, label="tailrl_cont_400") |
| for k, v in zip(k_values, last_maxrl): |
| ax_right.annotate(f"{v:.3f}", (k, v), textcoords="offset points", xytext=(5, 6), |
| fontsize=8, color='steelblue') |
| for k, v in zip(k_values, last_tailrl): |
| ax_right.annotate(f"{v:.3f}", (k, v), textcoords="offset points", xytext=(5, -14), |
| fontsize=8, color='darkorange') |
|
|
| ax_right.set_xscale("log", base=2) |
| ax_right.set_xticks(k_values) |
| ax_right.set_xticklabels([str(k) for k in k_values]) |
| ax_right.set_xlabel("k", fontsize=12) |
| ax_right.set_ylabel("pass@k", fontsize=12) |
| ax_right.set_title("pass@k at Last Checkpoint", fontsize=13) |
| all_right = last_maxrl + last_tailrl + base_one_shot_vals |
| ax_right.set_ylim(min(all_right) * 0.9, max(all_right) * 1.15) |
| ax_right.legend(fontsize=10) |
| ax_right.grid(True, alpha=0.3) |
|
|
| plt.tight_layout() |
| plt.savefig("output/pass_at_k_comparison.png", dpi=150) |
| print("Saved output/pass_at_k_comparison.png") |
|
|
