Parameter-efficient finetuning (PEFT) has become the standard approach for adapting large language models, yet evaluations largely emphasize downstream accuracy while overlooking the retention of pretrained capabilities. We argue that PEFT should be assessed through the stability-plasticity dilemma: the trade-off between target-task adaptation and resistance to forgetting. We introduce PEFT-Arena, a benchmark that jointly measures downstream performance and general capability retention. Across the studied settings, methods exhibit distinct stability-plasticity profiles, and orthogonal finetuning often lies on a favorable frontier under comparable parameter budgets. We analyze the internal changes associated with these external trade-offs from two complementary geometric perspectives. In weight space, spectral profiles characterize how parameterizations interact with the pretrained singular-value structure. In activation space, representation-similarity diagnostics show a strong association between forgetting and non-isometric distortion on general-evaluation data. Together, these views suggest that preserving pretrained spectral and representational geometry is an important inductive bias for retention, with OFT’s spectrum-preserving updates providing a particularly clear case. Finally, parameterization-aware interpolation shows that final SFT checkpoints can lie beyond a better target-retention operating point. We use post-hoc rewinding as a case study of controlling this trade-off.

Currently, most multimodal studies are based on large language models (LLMs) with quadratic-complexity Transformer architectures. While linear models like RNNs enjoy low inference costs, their application has been largely limited to the text-only modality. This work explores the capabilities of modern RNN architectures in multimodal contexts. We propose ModRWKV—a decoupled multimodal framework built upon the RWKV7 architecture as its LLM backbone—which achieves multi-source information fusion through dynamically adaptable heterogeneous modality encoders. We designed the multimodal modules in ModRWKV with an extremely lightweight architecture and, through extensive experiments, identified a configuration that achieves an optimal balance between performance and computational efficiency. ModRWKV leverages the pretrained weights of the RWKV7 LLM for initialization, which significantly accelerates multimodal training. Comparative experiments with different pretrained checkpoints further demonstrate that such initialization plays a crucial role in enhancing the model’s ability to understand multimodal signals. Supported by extensive experiments, we conclude that modern RNN architectures present a viable alternative to Transformers in the domain of multimodal large language models (MLLMs). Furthermore, we identify the optimal configuration of the ModRWKV architecture through systematic exploration.