Neural models are usually adapted through changes in parameters shared among model components via fine-tuning, alignment-based training, and reinforcement learning. These changes have been found effective in short-term optimization. However, they result in long-term alterations in the model's base behavior. In this study, we introduce the concept of structural irreversibility as a characteristic of shared-parameter model adaptation. This concept refers to the intertwining of task-specific objectives with the representational identity of the model. We show that when parameters are directly mutated, the resulting model behaves divergently from the original model. This divergence cannot be reversed deterministically without an explicit parameter snapshot. We introduce reversible behavioral learning, in which model behaviors are structurally dissociated from identity parameters and can be deterministically unloaded through an explicit unload process. We also introduce the Recoverability Factor as a normalized measure of behavioral recoverability and provide additional diagnostics based on model divergence. Experiments show that reversible model adaptation achieves rollback within numerical precision, whereas shared-parameter mutation exhibits persistent post-reset divergence.
Figures & tables
Figure 1: Exact rollback via behavioral elimination. (Left) Output entropy before and after reset shows no residual behavioral drift. (Right) Mean post-reset KL and JS divergence under increasing elimination rate ε . Both metrics exhibit a sharp threshold collapse at ε∗=0.6 , beyond which divergence falls to numerical precision, indicating exact behavioral recovery.
Elimination Rate ϵ
Post-reset KL
Post-reset JS
Recovery Regime
0.000
1.16×10−2
5.74×10−3
Partial
0.001
3.39×10−1
6.65×10−1
Partial
0.010
1.04×101
9.63×100
Partial
0.050
1.86×101
7.60×10−3
Partial
0.200
1.03×10−2
7.67×10−2
Partial
0.400
1.94×10−3
4.35×10−2
Partial
Table 1: Post-reset KL and JS divergence under increasing behavioral elimination rate ϵ .
Figure 2: Structural irreversibility under direct weight mutation. Post-reset KL and JS divergence increase monotonically with mutation intensity α for both 1.5B and 3B models. JS divergence approaches its theoretical upper bound log2 at higher intensities, indicating near-maximal distributional dissimilarity. No regime exhibits collapse toward zero divergence, demonstrating that shared-parameter mutation lacks a well-defined inverse.
α
1.5B
3B
RF
KL
JS
KL
JS
0.001
0.169
0.031
0.355
0.049
0
0.01
6.347
0.574
8.780
0.655
0
0.05
14.747
0.688
15.581
0.691
0
Table 2: Post-reset KL and JS divergence under increasing direct weight mutation intensity α for 1.5B and 3B models.
Adaptation Mechanism
Post-reset Divergence
RF
Direct Weight Mutation
>0
0
Structured Fine-Tuning
>0
0
Reversible Behavioral Adaptation (RLAE)
≈0
1
Table 3: Recoverability factor comparison across adaptation mechanisms. Weight-based adaptation exhibits zero recoverability (RF = 0), while reversible behavioral adaptation achieves exact recovery (RF = 1) within numerical precision limits.
Figure 3: Baseline output entropy across sprints. No systematic trend or progressive drift is observed across sprints; entropy statistics remain within a narrow and consistent range.
Sprint
Mean Entropy
Std. Dev.
Max Entropy
Sprint-1
0.245
0.064
0.359
Sprint-2
0.234
0.083
0.328
Sprint-3
0.261
0.071
0.374
Sprint-4
0.252
0.081
0.441
Table 4: Baseline entropy statistics across sprints.
Figure 4: Recoverability across model scales. Reversible behavioral adaptation remains invariant, while weight mutation degrades with scale.
Model Scale
Adaptation Method
Recoverability (%)
1.5B
RLAE
100
1.5B
Weight Mutation
30
3B
RLAE
100
3B
Weight Mutation
40
7B
RLAE
100
7B
Weight Mutation
10
Table 5: Post-reset divergence and recoverability across model sizes.