Relearning dynamics depend on unlearning strength: agents with 10% unlearning recover within early interaction rounds, while 30% and 50% agents show prolonged low-accuracy plateaus and delayed improvement¶
Claim · All claims
Evidence · 1 study · causalr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person
Subclaims¶
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q2 i? Lower-unlearning agents exhibit early performance recovery and steady accuracy increases; higher-unlearning agents show prolonged plateaus, sporadic correct responses, and delayed improvement. → Jiajia Song 2026
Evidence¶
Jiajia Song 2026¶
Jiajia Song, Zhihan Guo, and Jionghao Lin. (2026). Simulating Novice Students Using Machine Unlearning and Relearning in Large Language Models. https://arxiv.org/abs/2603.26142 doi:10.48550/arxiv.2603.26142
q2 · i? · causal · r2
Qualitative analysis of interaction trajectories (Fig. 5, Exception Handling topic) under LLM-coach instruction: 10%-unlearning agents "begin toexhibitperformancerecoverywithintheearlyinteractionrounds" while 30% and 50% agents show prolonged low-accuracy plateaus and delayed improvement.
"teachable agents with a lower unlearning ratio (10%) begin toexhibitperformancerecoverywithintheearlyinteractionrounds,followedbya steady increase in both rolling and cumulative accuracy."
Discussion¶
Related Claims¶
- Coach-guided interactive teaching yields higher and more progressive recovery than static relearning, particularly under moderate forgetting levels (10%–30%) — a broader claim this one bears on
- Teachable agents measurably but incompletely relearn unlearned knowledge, with the recovery gap to the base model widening under higher unlearning ratios (30%–50%) — a broader claim this one bears on
- Machine unlearning induces progressively stronger novice-level performance in an LLM teachable agent, with accuracy and F1 declining as the unlearning ratio increases — related
- Almost no model-and-harness combination sustains tutoring over the full 30-day horizon, as learning plateaus within 5–10 days — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
unlearning-ratio-moderates-relearning-trajectory |
title |
Relearning dynamics depend on unlearning strength: agents with 10% unlearning recover within early interaction rounds, while 30% and 50% agents show prolonged low-accuracy plateaus and delayed improvement |
description |
Relearning dynamics depend on unlearning strength: agents with 10% unlearning recover within early interaction rounds, while 30% and 50% agents show prolonged low-accuracy plateaus and delayed improvement |
status |
review |
evidence_strength |
weak |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — jiajia-song-2026 |
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