OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
Organization
Dill Lab at USC
dill-lab
Models in Library20
Datasets in Library0
Models on Hugging Face74
Followers8
Models
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…