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Reward Modeling Evaluation on RM-Bench

85.3Chat ScoreFlexible Principles ScalarRM
42.76453.80764.8575.893Apr 14, 2025Jun 23, 2025Sep 1, 2025Nov 10, 2025Jan 19, 2026Mar 30, 2026Jun 8, 2026

Evaluation results

MethodLinks
Flexible Principles ScalarRMRM Type (Scalar vs Generative)=Scalar, Latency (seconds/task)=<0.1 second/task2025.0985.381.970.496.985.584.980.583.6
Flexible Principles GenRMRM Type (Scalar vs Generative)=Generative, Latency (seconds/task)=>10 seconds/task2025.0980.4927795.588.986.483.486.2
DSV4 + Eval-SkillJudge Backbone=DeepSeek-V4-Flash, Rubric Model=w/o2026.0679.6195.679.9596.12———87.82
DSV4 + Naive SkillJudge Backbone=DeepSeek-V4-Flash, Rubric Model=w/o2026.0678.0691.6683.3396.68———87.43
Rubric-ARM-8B + Eval-SkillJudge Backbone=Qwen3-8B and Variants, Rubric Model=w/o2026.0677.7879.0762.4191.1———77.59
DSV4Judge Backbone=DeepSeek-V4-Flash, Rubric Model=DSV42026.0676.7979.6976.4890———80.74
RewardAnything-8B-v1RM Type (Scalar vs Generative)=Generative, Latency (seconds/task)=>10 seconds/task2025.0976.790.375.290.285.682.281.583.1
R3-QWEN3-14B-LORA-4KRM Type (Scalar vs Generative)=Generative, Latency (seconds/task)=>10 seconds/task2025.0976.592.478.791.991.486.277.184.9
Qwen-Instruct-32B-OursReward Model Type=ReasonRM2026.0475.68066.594.28680.870.779.2
Llama-3.3-Nemotron-70B-RewardRM Type (Scalar vs Generative)=Scalar, Latency (seconds/task)=<0.1 second/task2025.0975.484.569.390.492.184.163.579.9
Qwen-Instruct-14B-OursReward Model Type=ReasonRM2026.0475.375.660.993.382.977.768.576.4
RM-R1(Qwen2.5-32B)Training=GRPO, Data=RM-R12026.0575.2781.3964.8193.6785.6981.0169.6578.79
RM-R1(Qwen2.5-14B)Training=GRPO, Data=RM-R12026.0575.176.6860.1885.4681.1775.2366.6774.36
DSV4Judge Backbone=DeepSeek-V4-Flash, Rubric Model=w/o2026.0674.9678.372.0592.6———79.48
Qwen3-8B + Eval-SkillJudge Backbone=Qwen3-8B and Variants, Rubric Model=w/o2026.0674.482.3659.9892.6———77.34
RM-R1-DeepSeek-Distilled-Qwen-32BRM Type (Scalar vs Generative)=Generative, Latency (seconds/task)=>10 seconds/task2025.0974.291.874.195.489.585.476.783.9
SKYWORK-RM (w/ calibration)Mismatch Degree=0.639, Calibration setting=w/ calibration2025.0473.962.453.995.889.475.849.371.5
Llama-3.3-Nemotron-Super-49B-GenRMRM Type (Scalar vs Generative)=Generative, Latency (seconds/task)=>10 seconds/task2025.0973.791.47590.691.285.771.282.7
Bradley-TerryRM Type (Scalar vs Generative)=Scalar, Latency (seconds/task)=<0.1 second/task2025.0973.682.766.191.489.28264.278.5
SteerRMModel=URM2026.0372.96254.693.678.3736170.8
SteerRMModel=Skywork2026.0372.562.5569583.974.156.571.5
BaselineModel=URM2026.0372.261.653.494.783.973.753.770.5
DynaCFBackbone=Qwen3-8B2026.0672.17162.691.390.47854.474.2
SteerRMModel=Base-RM2026.0371.359.356.589.681.371.854.469.2
Llama-3-OffsetBias-RM-8BReward Model Type=Scalar RM2026.0471.361.953.289.684.672.250.269
Nemotron-340B-RewardReward Model Type=Scalar RM2026.0471.259.859.487.58171.456.169.5
URM-LLaMa-3.1-8BReward Model Type=Scalar RM2026.0471.261.854.193.18473.25370
URM-Llama-3.1-8BBackbone=Llama-3.1-8B2026.0671.261.854.193.18473.25370
DynaCFBackbone=Qwen3-4B2026.0671.269.263.488.791.178.25073.1
Rubric-ARM-8BJudge Backbone=Qwen3-8B and Variants, Rubric Model=Rubric-ARM-8B2026.0671.1770.7363.9868.33———69.06
RRM-32BJudge Backbone=Other Post-Trained RMs, Rubric Model=w/o2026.0671.0388.1971.4491.82———80.62
BaselineModel=Base-RM2026.037159.658.390.885.573.35169.9
Qwen3-8B + Naive SkillJudge Backbone=Qwen3-8B and Variants, Rubric Model=w/o2026.0670.8972.0557.8190.52———72.82
Llama-3.1-Nemotron-70B-RewardRM Type (Scalar vs Generative)=Scalar, Latency (seconds/task)=<0.1 second/task2025.0970.764.357.490.392.576.443.170.7
RM-R1(Qwen3-4B)Training=GRPO, Data=RM-R12026.0570.6372.1957.2192.2881.4473.9963.7573.06
BaselineModel=Skywork2026.0369.860.654.596.588.974.947.370.3
Skywork-Reward-Llama-3.1-8BReward Model Type=Scalar RM2026.0469.560.654.595.78974.746.670.1
Skywork-Reward-Gemma-2-27BBackbone=Gemma-2-27B2026.0669.554.753.291.97869.254.967.3
Skywork-Reward-Llama-3.1-8BBackbone=Llama-3.1-8B2026.0669.560.654.595.78974.746.670.1
SKYWORK-RM (w/o calibration)Mismatch Degree=0.639, Calibration setting=w/o calibration2025.0468.961.953.195.988.573.847.670
SKYWORK-RMMismatch Degree=0.639, Calibration setting=Original2025.0468.76252.895.988.473.747.569.9
tulu-v2.5-dpo-13b-hh-rlhf-60kReward Model Type=GenRM2026.0468.451.152.376.553.66369.662.1
Bradley–TerryBackbone=Qwen3-8B2026.0668.169.860.890.290.376.649.872.2
Qwen3-4BJudge Backbone=Qwen3-4B and Variants, Rubric Model=w/o2026.0667.9370.1454.1785.24———69.37
SteerRMModel=Tulu2026.0367.760.460.883.486.473.644.268.1
Qwen3-4B + Eval-SkillJudge Backbone=Qwen3-4B and Variants, Rubric Model=w/o2026.0667.6579.6762.8487.88———74.51
BaselineModel=QRM2026.0367.463.25295.687.873.447.569.5
RM-R1(Qwen2.5-7B)Training=GRPO, Data=RM-R12026.0567.2669.4155.2692.1880.9671.8760.2671.03
stablelm-2-12b-chatReward Model Type=GenRM2026.0467.254.951.665.269.163.546.659.7
SteerRMModel=Ins-RM2026.0367.165.159.991.187.175.150.170.8
RM-R1-DS-Qwen-7BJudge Backbone=Other Post-Trained RMs, Rubric Model=w/o2026.0667.0981.5954.8680.03———70.89
Qwen3-8BJudge Backbone=Qwen3-8B and Variants, Rubric Model=w/o2026.0666.9570.459.9890.22———71.89
Qwen-Instruct-7B-OursReward Model Type=ReasonRM2026.0466.966.854.492.979.571.359.570.1
BaselineModel=Ins-RM2026.0366.8655791.690.775.843.870.1
tulu-v2.5-dpo-13b-stackexchange-60kReward Model Type=GenRM2026.0466.449.954.26979.56337.259.9
tulu-2-dpo-13bReward Model Type=GenRM2026.0466.451.451.885.486.966.737.763.8
GRM-RM (w/ calibration)Mismatch Degree=0.508, Calibration setting=w/ calibration2025.0466.262.65889.384.973.948.369
Qwen3-4B + Naive SkillJudge Backbone=Qwen3-4B and Variants, Rubric Model=w/o2026.0665.6872.5756.8688.53———70.91
SteerRMModel=QRM2026.0365.562.952.895.281.171.254.969.1
Bradley–TerryBackbone=Qwen3-4B2026.0665.567.362.680.591.374.441.369
tulu-v2.5-dpo-13b-chatbot-arena-2023Reward Model Type=GenRM2026.0464.952.350.562.382.860.229.557.5
BaselineModel=Tulu2026.0364.860.257.783.591.973.833.966.5
Rubric-RM-8BJudge Backbone=Qwen3-8B and Variants, Rubric Model=Rubric-RM-8B2026.0664.769.8560.554.19———62.31
FSFAIRX-RM (w/ calibration)Mismatch Degree=0.554, Calibration setting=w/ calibration2025.0464.563.3569087.572.645.468.5
Qwen3-4BJudge Backbone=Qwen3-4B and Variants, Rubric Model=Rubric-RM-4B2026.0663.7166.2355.4769.01———63.61
Qwen3-8BJudge Backbone=Qwen3-8B and Variants, Rubric Model=Rubric-ARM-8B2026.0663.6367.6857.2977.48———66.52
GRM-RMMismatch Degree=0.508, Calibration setting=Original2025.0463.66256.989.182.271.849.667.9
GRM-RM (w/o calibration)Mismatch Degree=0.508, Calibration setting=w/o calibration2025.0463.662.458.389.582.972.749.868.4
Qwen3-8BJudge Backbone=Qwen3-8B and Variants, Rubric Model=Rubric-RM-8B2026.0663.4367.9156.9475.53———65.95
MISTRAL-RM (w/ calibration)Mismatch Degree=0.528, Calibration setting=w/ calibration2025.0463.25752.488.389.569.836.365.2
FSFAIRX-RM (w/o calibration)Mismatch Degree=0.554, Calibration setting=w/o calibration2025.0463.163.453.690.485.471.645.867.6
internlm2-20b-rewardReward Model Type=Scalar RM2026.0463.166.856.786.582.671.650.768.3
Qwen3-8BJudge Backbone=Qwen3-8B and Variants, Rubric Model=DSV42026.0662.7371.7258.3389.41———70.55
GRM-llama3-8B-sftregReward Model Type=Scalar RM2026.0462.762.557.89083.572.748.668.2
FSFAIRX-RMMismatch Degree=0.554, Calibration setting=Original2025.0462.563.254.690.486.571.644.967.7
RRM-7BJudge Backbone=Other Post-Trained RMs, Rubric Model=w/o2026.0662.3175.2453.7380.06———67.84
Qwen3-4BJudge Backbone=Qwen3-4B and Variants, Rubric Model=DSV42026.0662.1771.3857.8187.68———69.76
RM-R1-Qwen2.5-7BJudge Backbone=Other Post-Trained RMs, Rubric Model=w/o2026.0661.7458.6453.380.51———63.55
internlm2-7b-rewardReward Model Type=Scalar RM2026.0461.771.449.785.585.470.745.167.1
MISTRAL-RM (w/o calibration)Mismatch Degree=0.528, Calibration setting=w/o calibration2025.0461.457.45388.986.768.84065.2
MISTRAL-RMMismatch Degree=0.528, Calibration setting=Original2025.0460.856.652.688.788.368.237.564.7
BTRM-RM (w/ calibration)Mismatch Degree=0.162, Calibration setting=w/ calibration2025.0460.260.553.889.692.171.134.866
BTRM-RMMismatch Degree=0.162, Calibration setting=Original2025.046061.353.889.990.77137.166.3
Eurus-RM-7bReward Model Type=Scalar RM2026.0459.960.256.986.587.270.240.265.9
Nous-Hermes-2-Mistral-7B-DPOReward Model Type=GenRM2026.0458.855.651.373.969.561.149.159.9
BTRM-RM (w/o calibration)Mismatch Degree=0.162, Calibration setting=w/o calibration2025.0458.561.554.189.191.370.835.365.8
tulu-v2.5-70b-preference-mix-rmReward Model Type=Scalar RM2026.0458.251.455.587.172.865.650.763
ActivationModel=URM2026.0358.149.747.751.820.354.280.951.8
ActivationModel=Skywork2026.035850.35050.120.85481.552.1
RM-Mistral-7BReward Model Type=Scalar RM2026.0457.45752.787.288.667.134.963.5
Mistral-7B-instruct-Unified-FeedbackReward Model Type=Scalar RM2026.0456.55851.786.887.167.335.363.2
steerlm-70bReward Model Type=Scalar RM2026.0456.45349.351.248.354.954.352.5
tulu-v2.5-dpo-13b-nectar-60kReward Model Type=GenRM2026.0456.352.452.673.886.764.325.458.8
ActivationModel=Ins-RM2026.0355.851.546.284.765.160.952.659.5
RM-R1(Qwen3-0.6B)Training=GRPO, Data=RM-R12026.0555.0461.7353.6563.2473.7558.3643.1458.42
ActivationModel=Tulu2026.0352.550.149.144.353.948.644.449
Rubric-RM-4BJudge Backbone=Qwen3-4B and Variants, Rubric Model=Rubric-RM-4B2026.0648.148.5951.8263.83———53.09
ActivationModel=QRM2026.0347.949.447.24646.846.449.647.6
ActivationModel=Base-RM2026.0344.449.447.855.343.148.755.749.2