Natural Language Understanding on GLUE (test val)

94MRPC AccuracyFull-FT

Evaluation Results

Method
Full-FT2025.12	94	96.4	68	86.6	92.4	90.2	94.7	92.2	89.3
LoRA2025.12	93.4	96.2	68.2	87.4	92.6	90.6	94.9	91.6	89.4
GRASP2025.12	93	96.1	67	85.9	91.8	88.9	94.5	89.8	88.4
ModernALBERT-large2025.12	92.7	95.5	66.4	88.44	92.1	88.9	93.7	92	88.72
Full-FT2025.12	92.5	94.2	61.1	77.4	90.6	86.7	92.3	91.9	85.8
ResLoRAis2024.02	92.39	95.64	65.54	83.03	91.97	89.97	94.34	90.91	—
ModernBERT2025.12	92.2	96	65.1	87.4	91.8	89.1	93.9	92.1	88.45
GRASP2025.12	92.2	94.3	64.2	79.1	90.9	85.2	92	89.5	85.9
GTE-en-MLM2025.12	92.1	93.4	57	84.8	90.2	86.7	91.9	88.8	85.99
BitFit2025.12	92	93.7	61.8	77.8	90.8	85	91.3	87.3	84.9
LoRA2024.02	91.96	95.99	67.49	83.03	92.14	90.59	94.2	91.68	—
ModernALBERT-base2025.12	91.7	95.2	64.6	85.6	91.8	88.2	93	91.5	87.7
QuanTA2024.05	91.67	93.81	62.08	77.26	90.68	—	—	—	—
ResLoRAms2024.02	91.64	95.76	65.8	81.95	91.11	88.74	94.34	87.14	—
GRASP2025.12	91.5	93.8	61.9	77.2	90.3	83.4	90.4	88.2	84.6
LoRA2024.05	91.48	94.01	62.08	74.51	90.48	—	—	—	—
ResLoRAbs2024.02	91.31	96.22	65.44	82.31	91.72	90.4	94.48	91.27	—
ADAM2026.02	91.3	94.57	62.24	79.42	90.92	87.18	92.33	92.28	86.28
LoRA2024.02	91.13	95.3	65.29	79.06	91.67	90.26	94.23	91.01	—
ModernALBERT-medium2025.12	91	94	62.3	81.6	91.2	86.6	92	91	86.21
MELORA2024.02	90.93	95.41	64.09	86.64	91.93	87.2	93.17	90.77	87.52
RoBERTa_LARGE2024.09	90.9	96.4	68	86.6	—	90.2	94.7	92.2	88.43
VeRA2025.12	90.9	96.1	68	85.9	91.7	—	94.4	—	—
MELORA2024.02	90.69	94.95	64.07	86.28	91.08	86.21	92.7	89.26	86.91
TSR2026.02	90.38	93.92	61.32	77.98	90.72	86.88	92.95	90.16	85.54
RED2025.12	90.3	96	68.1	86.2	91.3	89.5	93.5	88.9	87.9
GALORE2026.02	90.27	93.92	59.33	77.98	90.82	87.11	92.42	89.54	85.17
RoBERTa2025.12	90.2	94.8	63.6	78.7	91.2	87.6	92.8	91.9	86.35
ModernALBERT-tiny2025.12	90.2	93	58.4	81.2	90.4	84.6	91.3	90.5	84.95
AdaLora2024.02	90.19	94.49	61.64	85.19	91.16	87.34	93.08	90.14	86.65
DeltaLoRA2024.02	90.19	95.06	63.82	87	91.57	87.5	93.09	90.87	87.38
LoRA2024.02	89.92	94.38	62.43	85.92	91.36	86.91	92.64	90.78	86.79
LoRA2025.12	89.7	95.1	63.4	86.6	91.5	87.5	93.3	90.8	87.2
BERT2025.12	89.5	93.1	59	78.2	89.4	85.4	91.6	91.4	84.84
DeBERTav32025.12	89.5	95.6	69.2	83.8	91.6	90	94	92.4	88.26
DyLoRA2024.02	89.46	94.26	61.12	84.47	91.06	86.33	92.22	90.17	86.14
RED2025.12	89.2	93.9	61	78	90.4	83.9	90.7	87.2	84.3
MosaicBERT-1282025.12	89	93.5	58.2	83	90.3	85.6	91.4	92	85.38
LoHA2024.02	88.85	95.76	60.57	55.23	90.21	89.63	94.03	89.93	—
Adapters2025.12	88.5	94.2	60.8	71.5	89.7	87.1	93.1	90.2	84.4
FT2024.02	88.23	94.26	64.57	84.11	90.56	87.51	92.73	91.96	86.74
BERT_LARGE2024.09	88	93.2	60.6	70.4	—	86.6	92.3	91.3	83.2
NomicBERT-20482025.12	88	93	50	82	90	86	92	92	84.13
Expert (upper bound)2025.12	86.8	—	43.4	58.1	—	—	76.2	—	66.1
(IA)32025.12	86.4	93.4	57.8	73.5	88.5	85.4	91.1	88.5	83.1
LoKr2024.02	81.41	94.5	55.22	52.71	83.35	87.08	92.44	88.3	—
AdaLoRA2024.02	81.22	95.3	57.01	52.71	89.76	89.11	94.29	89.77	—
Fisher-1002025.12	80.1	—	22.9	47.7	—	—	42.6	—	48.3
Fisher-5002025.12	80	—	23.2	46.9	—	—	40.8	—	47.7
UMTAM2025.12	79.4	—	17.7	45.1	—	—	45.4	—	46.9
TIES (baseline)2025.12	79.2	—	12.3	46.2	—	—	46	—	45.9
OTA-style2025.12	79	—	25.7	46.6	—	—	41.4	—	48.2
Fisher-Train2025.12	78.5	—	12.7	45.1	—	—	46.4	—	45.7
GPT-4o mini + SENSE2024.09	76.47	92.08	67.22	88.45	—	78.2	93.3	73	81.25
GPT-3.5 turbo + SENSE2024.09	75.49	92.2	64.57	84.12	—	64.6	83.2	77.2	77.34
LLaMA3-70B + SENSE2024.09	74.04	95.18	65.53	85.56	—	73.1	92.8	76.5	80.25
LLaMA3-70B2024.09	73.52	95.64	63.9	84.48	—	71.9	91.3	74.6	79.34
GPT-3.5 turbo2024.09	73.28	91.86	63.5	81.81	—	61.8	82.4	73.4	75.44
GPT-4o mini2024.09	72.3	91.63	65.49	87.36	—	73.9	92.3	73	79.43