pith:RWT45WHE
Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning
Sparse Memory Finetuning updates only the most heavily read rows in added key-value layers to gain task performance while preserving general capabilities better than LoRA or full finetuning.
arxiv:2605.03229 v2 · 2026-05-04 · cs.CL · cs.LG
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Record completeness
Claims
SMF improves MedMCQA by 2.5 percentage points while keeping both forgetting probes within roughly 1 point of the base model, whereas LoRA and full finetuning achieve larger gains but with clear drift on both.
That selectively updating only the most heavily read memory rows during training is sufficient to acquire new task knowledge without unintended interference in unrelated general capabilities.
SMF improves MedMCQA accuracy by 2.5 points while keeping WikiText perplexity and TriviaQA accuracy within 1 point of the base model, outperforming LoRA and full finetuning on forgetting metrics.
Formal links
Receipt and verification
| First computed | 2026-06-09T02:08:43.618522Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8da7ced8e41c353ffe999ca7d02e218cb8181a55c59ecfcb1bb850111953d964
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/RWT45WHEDQ2T77UZTST5ALRBRS \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 8da7ced8e41c353ffe999ca7d02e218cb8181a55c59ecfcb1bb850111953d964
Canonical record JSON
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