Kimia Nadjahi, Pierre Ablin, Gabriel Peyré, Valérie Castin
We lifted 8 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| vcastin/balora | canonical | 8 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| apply_linear | Ran | vcastin/balora/synthetic/layers.py code served (permissive licence) · get_code("8a479c466f315823") |
| create_preconditioner | Ran | vcastin/balora/lora_rite.py code served (permissive licence) · get_code("5ed0f5c52db3d133") |
| format_ai2_arc_messages | Ran | vcastin/balora/dataset_utilities/ai2_arc_utilities.py code served (permissive licence) · get_code("93e81f47bfc515e3") |
| format_alpaca_messages | Ran | vcastin/balora/dataset_utilities/alpaca_utilities.py code served (permissive licence) · get_code("c11f6c51712ec176") |
| format_code_feedback_messages | Ran | vcastin/balora/dataset_utilities/code_feedback_utilities.py code served (permissive licence) · get_code("f29b817f83493fae") |
| init_linear | Ran | vcastin/balora/synthetic/layers.py code served (permissive licence) · get_code("bfe965559d748570") |
| init_linear_network | Ran | vcastin/balora/synthetic/layers.py code served (permissive licence) · get_code("ff7851c957801640") |
| preprocess_instruction_dataset | Ran | vcastin/balora/dataset_utilities/common_utilities.py code served (permissive licence) · get_code("a853c7022ed84258") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Low-Rank Adaptation (LoRA) is the most widely adopted method for fine-tuning large language models. Notably, LoRA is inherently overparameterized: multiple pairs of low-rank factors can yield the same adapted weight matrix. We show-both theoretically and empirically-that these pairs exhibit significantly different condition numbers. As a result, converging to different loss minimizers directly impacts the convergence rate of LoRA. Building on this observation, we introduce Balanced Low-Rank Adaptation (BaLoRA), a variant of LoRA that projects iterates onto a balanced manifold. This manifold improves the conditioning of the loss landscape while preserving the adapted matrix. The projection step is computationally lightweight and integrates seamlessly into existing fine-tuning pipelines. Empirically, BaLoRA converges faster than standard LoRA and achieves superior performance across a range of fine-tuning tasks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2605.31484")
get_code_for_paper("2605.31484")
have("2605.31484")
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