{
  "assessment": "verified",
  "claim": 5,
  "destructive_control": {
    "effect": "the unweighted baseline is 4-6x farther from the independently sampled fresh expectation",
    "mutation": "discard SOSMC importance weights and use the unweighted ImpDiff particle mean",
    "tracking_error_reduction_factors_from_weighting": [
      4.174582065578795,
      5.547755280665158
    ]
  },
  "literal_claim": "On 2D EBM benchmark datasets, SOSMC achieves higher objective values than ImpDiff for small regularization strengths, with particle-based reward estimates that track true expectations throughout optimization (Section 5.2).",
  "released_native_artifact_runs": [
    {
      "beta_kl": 0.1,
      "final_impdiff_fresh_reward": 0.9949566426873208,
      "final_sosmc_fresh_reward": 0.9985862391233444,
      "fresh_eval_steps": [
        0,
        1000,
        2000,
        3000,
        4000,
        5000
      ],
      "impdiff_particle_to_fresh_mae": 0.05335863728920618,
      "outer_iterations": 5001,
      "particles": 10000,
      "sosmc_fresh_reward_advantage": 0.0036295964360236743,
      "sosmc_weighted_particle_to_fresh_mae": 0.012781791434685367,
      "tracking_error_reduction_factor": 4.174582065578795
    },
    {
      "beta_kl": 0.25,
      "final_impdiff_fresh_reward": 0.9689168484330177,
      "final_sosmc_fresh_reward": 0.9957832426309585,
      "fresh_eval_steps": [
        0,
        1000,
        2000,
        3000,
        4000,
        5000
      ],
      "impdiff_particle_to_fresh_mae": 0.07853053498864175,
      "outer_iterations": 5001,
      "particles": 10000,
      "sosmc_fresh_reward_advantage": 0.02686639419794079,
      "sosmc_weighted_particle_to_fresh_mae": 0.014155371139446907,
      "tracking_error_reduction_factor": 5.547755280665158
    }
  ]
}