{"id":"150a1d8e-f40a-4e51-b874-eb885b717170","arxiv_id":"2606.24752","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Plasticity loss in GPT-style transformers on multilingual tasks persists from 5M to 314M parameters, follows a sublinear scaling law with model size, and occurs in both continual and stationary settings.","lead":"This paper studies whether scaling up GPT-style transformer models prevents loss of plasticity—the ability to learn new information after prior training—on a multilingual continual learning task. It finds that plasticity loss still occurs up to 314M parameters, with onset delayed sublinearly by size but not eliminated, and also appears in stationary training.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Validity of held-out Vietnamese probing deterioration as plasticity loss measure is unverified","rationale":"The reader's weakest assumption directly identifies the measurement foundation of the empirical claims; the abstract-only limitation noted by the reader is now addressable with full text, but the concern remains load-bearing until the protocol is confirmed to match the plasticity definition.","tokens_in":1758,"tokens_out":309,"duration_ms":23553,"concrete_test":"In the methods, locate the exact Vietnamese probing protocol (§ on experimental setup or evaluation); check whether it includes a post-training fine-tuning stage on Vietnamese data to measure learning speed/gradient norms versus a baseline, or only reports held-out loss during main training. If the former is absent, recompute all scaling plots using only models where adaptation rate was explicitly measured.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—that scale delays but does not eliminate plasticity loss, even in stationary training—rests entirely on interpreting performance drop on the Vietnamese probe as evidence of reduced ability to acquire new information. Standard plasticity loss requires showing slower adaptation rates when a new task is introduced after prolonged training; if the probe is only static loss evaluation on never-seen data (or without a subsequent adaptation phase), the observed deterioration could instead reflect representation drift, negative transfer, or multilingual interference. The scaling law and stationary-training results inherit this ambiguity, and model sizes (5M–314M) plus the specific multilingual regime may not generalize without confirming the metric.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper studies plasticity loss in GPT-style Transformer language models (5M–314M non-embedding parameters) trained on multilingual continual learning. It reports consistent deterioration on a held-out Vietnamese probing task as evidence of plasticity loss, identifies a sublinear scaling law governing the onset of this loss with model size, and finds similar deterioration under stationary multilingual training, concluding that scale delays but does not eliminate the problem.","tokens_in":1871,"tokens_out":481,"duration_ms":19756,"significance":"If the Vietnamese probe is shown to measure reduced adaptation capacity rather than other forms of drift, the sublinear scaling result and the stationary-training observation would indicate that parameter count alone is unlikely to solve plasticity loss in natural-language domains. This would strengthen the case for targeted interventions beyond scale in continual-learning LLM research.","major_comments":[{"comment":"Abstract and §3 (experimental setup): the central claim equates deterioration on the held-out Vietnamese probing task with plasticity loss, yet the standard definition requires demonstrating slower adaptation rates on a new task after prolonged training; static evaluation on never-seen data may instead reflect representation drift or multilingual interference, rendering the scaling law and stationary-training conclusions ambiguous.","section":"Abstract"},{"comment":"§4 (results): the reported sublinear scaling of plasticity-loss onset with model size is presented without error bars, confidence intervals, or controls for training-procedure confounds, so it is unclear whether the functional form is robust or an artifact of the specific multilingual regime and model sizes (5M–314M).","section":"§4"}],"minor_comments":[{"comment":"The abstract states the models have 5M–314M non-embedding parameters but does not specify embedding sizes or total parameter counts, which would aid reproducibility.","section":"Abstract"},{"comment":"No reference is made to prior work that directly measures adaptation rates (e.g., via fine-tuning curves) after long pre-training; adding such citations would clarify how the probe differs from standard plasticity metrics.","section":null}],"recommendation":"major_revision","confidential_remarks":"The manuscript's empirical focus on scaling is a strength, but the metric validity issue is load-bearing and should be addressed before acceptance."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the detailed and constructive comments. We address each major comment below and indicate the revisions we will make.","responses":[{"response":"We appreciate the referee's point on definitional precision. Our manuscript uses deterioration on the held-out Vietnamese probe as a practical proxy for plasticity loss in the multilingual setting, following the measurement approach in the cited prior work on language models. We acknowledge that this static evaluation does not directly demonstrate slower adaptation rates on a new task and could be influenced by drift or interference. To resolve the ambiguity, we will revise the abstract and §3 to explicitly frame the probe result as a proxy measure, add a limitations paragraph discussing alternative interpretations, and note that future work could include direct adaptation-rate experiments. This is a partial revision.","revision_made":"partial","referee_comment":"[Abstract] Abstract and §3 (experimental setup): the central claim equates deterioration on the held-out Vietnamese probing task with plasticity loss, yet the standard definition requires demonstrating slower adaptation rates on a new task after prolonged training; static evaluation on never-seen data may instead reflect representation drift or multilingual interference, rendering the scaling law and stationary-training conclusions ambiguous."},{"response":"We agree that the scaling-law figure and analysis would be strengthened by statistical rigor. In the revised manuscript we will recompute the onset points with error bars and confidence intervals obtained from multiple independent runs, and we will add a paragraph in §4 discussing controls for training-procedure variables (e.g., learning-rate schedules, data ordering) to show that the sublinear functional form is not an artifact of the particular regime.","revision_made":"yes","referee_comment":"[§4] §4 (results): the reported sublinear scaling of plasticity-loss onset with model size is presented without error bars, confidence intervals, or controls for training-procedure confounds, so it is unclear whether the functional form is robust or an artifact of the specific multilingual regime and model sizes (5M–314M)."}],"tokens_in":1357,"tokens_out":437,"duration_ms":19961,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's main result is that plasticity loss appears in these transformer LLMs on a Vietnamese probe task, with the onset scaling sublinearly with parameter count up to 314M, and that it occurs even in stationary multilingual training.\n\nWhat is new is the application to transformers in a multilingual continual and stationary setup, plus the scaling observation. Prior work was mostly on smaller models, so this extends the phenomenon. The stationary training finding is useful because it challenges the assumption that abrupt task changes are required.\n\nThe paper does a reasonable job of documenting the effect across sizes and settings.\n\nThe main soft spot is whether the held-out task deterioration actually measures plasticity loss. Plasticity loss typically means slower learning on new information after prolonged training, but here it seems based on performance drop without necessarily showing reduced adaptation rates. This could reflect other issues like negative transfer or drift. The models are not very large, and without more details on the training procedure or statistical controls, the scaling law is hard to assess fully. The concern about the metric seems to hold based on the abstract.\n\nThis is relevant for people studying continual learning or stability in LLMs. It is worth sending for peer review to get the methods clarified and the claims tested.","headline":"Plasticity loss in transformers scales sublinearly and persists in stationary training, but the evidence depends on a probe whose validity as a plasticity measure is unclear.","tokens_in":2392,"tokens_out":328,"would_cite":false,"duration_ms":22575,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Larger transformer language models delay the onset of plasticity loss but do not prevent it.","keywords":["plasticity loss","large language models","continual learning","scaling laws","transformers","multilingual training","stationary training","adaptation"],"falsifier":"Training models substantially larger than 314 million parameters on the same multilingual setup and finding no performance deterioration on the Vietnamese probing task after long training would contradict the claim that scale alone cannot eliminate plasticity loss.","tokens_in":2632,"feed_emoji":"","tokens_out":727,"duration_ms":14483,"temperature":0.7,"pith_summary":"The paper tests whether simply making GPT-style models bigger solves the long-known problem of networks losing the ability to learn new information after extended training. Researchers train models from 5 million to 314 million non-embedding parameters on multilingual data and measure how well they can still adapt by tracking performance drops on a held-out Vietnamese task. Plasticity loss appears at all tested sizes, with its appearance delayed according to a sublinear scaling law. The same loss occurs even when the training data distribution stays fixed with no abrupt task switches. These findings indicate that parameter scaling by itself will not keep large language models adaptable after sufficiently long training.","feed_headline":"Scale delays plasticity loss in LLMs but does not stop it","feed_subtitle":"Models from 5M to 314M parameters still lose adaptation ability on new languages, following a sublinear onset law even in stationary trainin","key_machinery":"Deterioration on a held-out Vietnamese probing task, used to quantify plasticity loss, together with the sublinear scaling law that describes when this deterioration begins as a function of model size.","core_discovery":"In GPT-style Transformer models trained on a multilingual continual learning problem, evidence of plasticity loss appears across scales from 5M to 314M non-embedding parameters as measured by deterioration on a held-out Vietnamese probing task. The onset of this loss follows a predictable scaling law that grows sublinearly with model size. The same deterioration is observed under stationary multilingual training without task changes, indicating that the phenomenon is not limited to abrupt distributional shifts.","pith_inferences":["Methods other than pure scaling, such as architectural changes or regularization techniques, will likely be needed to sustain long-term adaptability in deployed language models.","The sublinear delay pattern suggests that practical training runs of current-generation models may already encounter adaptation limits before reaching the largest feasible sizes.","Similar measurements on non-language domains could reveal whether the scaling behavior is specific to text or applies more broadly to neural networks.","Monitoring probing-task performance during pretraining could serve as an early warning signal for when plasticity begins to degrade."],"forward_implications":["Even models with hundreds of millions of parameters will eventually require additional mechanisms to maintain efficient adaptation after prolonged training.","Plasticity loss arises in both continual learning with task changes and in stationary training on a fixed data distribution.","Increasing parameter count postpones the measurable onset of plasticity loss according to a sublinear relationship.","Natural-language transformers will lose the capacity for efficient adaptation to new data after sufficiently long training regardless of scale."],"fun_headline_variants":["Plasticity loss persists across LLM scales from 5M to 314M","Sublinear law predicts plasticity loss onset in Transformers","Plasticity loss appears under stationary multilingual training","Larger LLMs delay but cannot avoid plasticity loss","Vietnamese probing confirms plasticity loss in GPT models"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Drops in performance on the held-out Vietnamese task serve as a sufficient and representative indicator of plasticity loss, and the multilingual regimes and model sizes examined reflect typical large language model training.","fun_headline_variants_meta":{"raw":{"variants":["Plasticity loss persists across LLM scales from 5M to 314M","Sublinear law predicts plasticity loss onset in Transformers","Plasticity loss appears under stationary multilingual training","Larger LLMs delay but cannot avoid plasticity loss","Vietnamese probing confirms plasticity loss in GPT models"]},"model":"grok-4.3","cost_usd":0.009057,"raw_usage":{"total_tokens":4080,"prompt_tokens":699,"num_sources_used":0,"completion_tokens":67,"cost_in_usd_ticks":90574500,"prompt_tokens_details":{"text_tokens":699,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3314,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":699,"tokens_out":67,"duration_ms":22918,"temperature":1.0,"reasoning_tokens":3314,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-25T23:24:44.172084+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Training models substantially larger than 314 million parameters on the same multilingual setup and finding no performance deterioration on the Vietnamese probing task after long training would contradict the claim that scale alone cannot eliminate plasticity loss.","supporting_citations":[],"review_version":1}