Hugging Face's logo

Datasets:
mercor
/
ApexAgentsRecipe-ApexAgents480-EvalTraces

Tasks:
Text Generation
Tags:
agents
apex-agents
trajectories
reinforcement-learning
License:
Dataset card Files
xet
 Community

You need to agree to share your contact information to access this dataset

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

This dataset contains evaluation traces on the APEX-Agents benchmark, including rubric criteria and judge rationales. Like the canonical mercor/apex-agents dataset, it is intended exclusively for model evaluation. Any use of this dataset for training, fine-tuning, or parameter fitting is forbidden. Crawling or scraping the dataset is also forbidden. By requesting access you agree to these terms.

Log in or Sign Up to review the conditions and access this dataset content.

ApexAgents Recipe — APEX-Agents 480 Eval Traces

Full evaluation trial dumps on the 480-task APEX-Agents benchmark for four model configurations: Qwen3.5-397B and Qwen3.6-35B, each before and after RL post-training with the ApexAgents SkyRL recipe. Each set contains 3 independent evaluation runs over all 480 tasks (one tarball per run).

Companion dataset: ApexAgentsRecipe-TBench2_1-EvalTraces (the same four models evaluated on Terminal-Bench 2.1).

License and intended use

CC-BY-4.0, with the same restriction as the canonical mercor/apex-agents benchmark: these traces (including rubric criteria and judge rationales embedded in grades) are for model evaluation only — using them for training, fine-tuning, or parameter fitting is forbidden, as is crawling or scraping.

Sets

Folder Model Checkpoint Runs
Qwen3p5-397B_untrained_traces Qwen3.5-397B-A17B base run1–3
Qwen3p5-397B_trained_traces Qwen3.5-397B-A17B post-trained run1–3
Qwen3p6-35B_untrained_traces Qwen3.6-35B-A3B base run1–3
Qwen3p6-35B_trained_traces Qwen3.6-35B-A3B post-trained pass1–3

Results

MeanReward is the average reward over the 480 tasks (rewards are fractional, in [0, 1]); Pass@1 is the fraction of trials with a perfect reward of 1.0. Both are the mean ± sample standard deviation over the 3 runs.

Set MeanReward Pass@1
Qwen3.5-397B untrained 31.29% ± 1.98 16.11% ± 1.48
Qwen3.5-397B trained 43.18% ± 0.93 27.29% ± 1.04
Qwen3.6-35B untrained 28.69% ± 1.29 13.96% ± 1.46
Qwen3.6-35B trained 38.69% ± 0.57 22.71% ± 0.83

Harness

All runs use the Archipelago MCP-based agent inside a Harbor-style trial runner: max_steps=250, temperature 1.0, top_p=1.0, top_k disabled, 262k-token context. The agent works through MCP tools (document readers, spreadsheets, chat/email servers, code execution) inside each task's simulated company world.

Layout

Each folder holds one gzipped tarball per evaluation run. Inside a tarball, every trial is a directory named <task_name>__<trial_id>/:

<task_name>__<trial_id>/
  config.json            trial configuration (task path, agent/model settings)
  result.json            full trial result (config, timing, exception info)
  trial.log              trial runner log
  agent/
    trajectory.json      the agent's message trajectory
    tito_transitions.json  token-in/token-out transitions (training-format rollout)
    start.log, tito_debug.txt
  verifier/
    reward.json          reward plus per-verifier judge grades and rationales
    grade_log.txt, test-stdout.txt, reward.txt
  artifacts/manifest.json

Reproducing the scores 

import glob, json, tarfile

for run_tgz in sorted(glob.glob("Qwen3p6-35B_trained_traces/*.tar.gz")):
    rewards = []
    with tarfile.open(run_tgz) as tf:
        for m in tf.getmembers():
            if m.name.endswith("verifier/reward.json"):
                rewards.append(json.load(tf.extractfile(m)).get("reward") or 0.0)
    mean = sum(rewards) / len(rewards)
    pass1 = sum(r == 1.0 for r in rewards) / len(rewards)
    print(f"{run_tgz}: n={len(rewards)} mean_reward={mean:.4f} pass@1={pass1:.4f}")
Downloads last month
13
Total file size:
22.6 GB

Collection including mercor/ApexAgentsRecipe-ApexAgents480-EvalTraces