Mapping Post-Training Forgetting in Language Models at Scale
Authors: Jackson Harmon
Andreas Hochlehnert
Matthias Bethge
Ameya Prabhu
Venue: ICLR 2026 Poster (2026)
Keywords: continual learning
foundation models
reasoning
forgetting
pretraining knowledge
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: We quantify forgetting of pretraining knowledge during post-training using simple samplewise metrics -- providing an extensive empirical analysis and open problems in continual learning for foundation models

Abstract: Scaled post‑training now drives many of the largest capability gains in language models (LMs), yet its effect on pretrained knowledge remains poorly understood. Not all forgetting is equal: Forgetting one fact (e.g., a U.S. president or an API call) does not “average out” when recalling another. Hence, we propose a sample-wise paradigm to measure what is forgotten and when backward transfer occurs. Our metric counts 1→0 transitions (correct before post‑training, incorrect after) to quantify forgetting and 0→1 transitions to quantify backward transfer. Traditional task averages conflate these effects and obscure large changes. For multiple‑choice benchmarks, we add chance‑adjusted variants that subtract the expected contribution of random guessing from pre‑ and post‑training accuracies. We apply this framework across post‑training stages, model sizes, and data scales. Our large‑scale analysis across nearly 30 model pairs and 100 sub-benchmarks with up to 32,768 generated tokens per sample shows that: (1) Domain-continual pretraining induces moderate forgetting with low-to-moderate backward transfer; (2) RL/SFT post-training applied to base models and instruction tuning yields moderate-to-large backward transfer on math and logic with overall low-to-moderate forgetting; (3) Applying RL/SFT to instruction‑tuned models is sensitive on data scale: at small scales, both forgetting and backward transfer are small; at larger scales, effects are mixed and warrant further study with better controls; (4) Model merging does not reliably mitigate forgetting. Overall, our framework offers a practical yardstick for mapping how post‑training alters pretrained knowledge at scale -- enabling progress towards generally capable AI systems.

Published as a conference paper at ICLR 2026
MAPPING POST-TRAINING FORGETTING IN LANGUAGE MODELS AT SCALE
Jackson Harmon
Matthias Bethge* Tübingen Center, University of Tübingen
Andreas Hochlehnert
Ameya Prabhu"
-ttta::/poottfooee....et 1o/
ABSTRACT
Scaled post-training now drives many of the largest capability gains in language models (LMs). yet its effect on pretrained knowledge remains poorly understood. Not all forgetting is equal: Forgetting one fact (e.g., U.S. president or an API call) does not "average out" when recalling another. Hence, we propose a sample-wise paradigm measure what is forgotten and when backward transfer occurs. Our metric counts 1 1->0 transitions (correct before post-training. incorrect after) quantify forgetting and (I+1 transitions to quantify backward transfer. Traditional task averages conflate these effects and obscure large changes. For multiple-choics benchmarks, we add chance-adjuse tion of random guessing from pre- and post-training accuracies. We apply this framework across post-training stages, model sizes, and data scales. Our large-scale analysis across nearly model pairs and 100 sub-benchmarks with up to 32,768 generated tokens per sample shows that: (1) Domain-continual pretraining induces moderate forgetting with low-to-moderate backward transfer; (2) RI /SFT post- training applied to base models and instruction tuning yields moderate-to-large backward transfer on math and logic with overall low-to-moderate forgetting; (3) Applying RL/SFT to instruction-tuned models is sensitive on data scale: at small scales, both forgetting and backward transfer are small; at larger scales, effects are mixed and warrant further study with better controls; (4) Model merging does not reliably mitigate forgetting. Overall, our framework offers practical yardstick for mapping how post-training alters pretrained knowledge at scale enabling progress towards generally capable Al systems.
variants that subtract the expected contribu-
INTRODUCTION
Scaling post-training has become the dominant driver capability gains in modern language models (LMs) (Jaech al.) 2024). Practitioners now iterate through multi-step post-training pipelines often at data scales that rival early pretraining (Tie et al. 2025). The implicit bet is that each step in the pipeline accumulates new capabilities, with dramatic improvements in areas like coding, math, tool use and safety, without sacrificing broad world knowledge. In contrast, it is considered common knowledge in continual learning that sequential training leads catastrophic forgetting (see Table TT. We test this assumption: as we scale post-training, do we erode the very breadth of world knowledge that pretraining painstakingly compresses into the weights? If the implicit assumption does not hold, we risk trading generalist competence for narrow specialization, undermining progress toward generally capable models.
Measuring forgetting in modern post-training pipelines is tricky. Classical evaluations compare aggregate test accuracy before and after training, implicitly treating benchmark as single task with fungible i.idd samples (c.g., classifying images of cats). Pretrained knowledge violates this assumption. Knowing one U.S. president does not compensate for forgetting another; recalling NumPy broadcasting rule does not offset losing knowledge of specific cloud-API syntax. In short, knowledge samples are fungible: each carries unique value for quantifying pretraining knowledge. Aggregation can hide substantial losses. Hence, we measure forgetting and backward transfer in sample- sample-wise wise manner, rather than at the task level proposed by Lopez-Pat Paz & Ranzato (2017).
"Equal Supervision

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Published as a conference paper at ICLR 2026
Specifically, define forgetting as items that are answered correctly before post-training stage but incorrectly afterward (the 1->0 0 transitions). and backward transfer as items that are answered incorrectly before but correctly after post-training (the 0-> 1 transitions). A further complication is that most knowledge-imttnsive LLM evaluation benchmarks are multiple-choice Random guessing inflates accuracy and can create illusory transitions: apparent "1 - 0" may simply be lucky guess that later becomes an incorrect answer, even when the underlying knowledge did not change; likewise for 0) 00+1 transitions. When the answer is only among few options (c.g., 4), performance by random guessing can account for a substantial share of observed transitions, distorting both level and trend estimates of forgetting. Thus principled metric should (i) resolve outcomes at the item level and (ii) explicitly correct for chance.
To account for these considerations, introduce chance-adjuste metrics for forgetting (Fuue) and backward transfer (BTT..), which correct for transitions expected under random choice. They do not need logits repeated sampling, requiring only the number of answer choices, the sample-level accuracies, and the marginal accuracy before and after training, making them practical at scale. Intuitively, chance-adjusted forgetting asks: among items the model genuinely knew before, what fraction became wrong beyond chance? Conversely, chance-adjusted backward transfer asks: among items the model genuinely did not correctly solve, what fraction became correct beyond chance?
Our primary contribution is large-scale study measuring forgetting caused by post-training across post-training pipelines. By evaluating the models on the same set of samples before and after each stage, we obtain a map of what was retained, what was forgotten, and where losses concentrate. We seek answer three questions: (i) Where in the pipeline is forgetting most pronounced (c.g.. instruction tuning vs. reasoning-focuse most affected (culture logic)?, and (iii) How much knowledge is forgotten re-clicited? We have the following key findings:
training)?. . (ii) What kinds pretraining knowledge are
Key Findings
Domain-Continusl Pretraining induces low to moderate forgetting across most categories; backward transfer is limited. Forgetting effects marginally decrease with increasing model scale.
Instruction-Tuning and SFT/RL from base models yield low to moderate forgetting, with spikes in the Culture and Knowledge categories, but moderate high (for SFT/RL from Base)
gains the Math and Logic categories across model families; forgetting and backward transfer decrease as parameters increase. Reasoning training yields similar forgetting and larger backward transfer than instruction tuning.
SFT/RL Reasoning Post-Traine Dost-Training from instruct models have data-scale dependent behaviour: For the low-data regime, it yields low forgetting and backward transfer. For the high-data regime, no dominant factor robustly explains the forgetting and backward transfer dynamics.
Model Merging does not reliably mitigate forgetting across post-training pipelines (yet).
Table 1: Catastrophic forgetting literature across LLM post-training stages. Continual learning literature indicates extensive forgetting across the post-training pipeline. However, we find far less forgetting when testing widely used post-training pipelines, indicating important gap existing between continual learning setups and how people post-train language models.
Stage
Name
Level
Summary
CPT
Investigating Continual Pretraining
Med
Most models show continual im- provement; only Llama-2 models degrade.
3.1
in LLMs: Insights and Implications
(Yildız et al. al.] (2024)
Examiing Forgetting in Continual
High
Continual pretraining degrades ca- pabilities, alignment and alters out- put behavior.
Pretraining of Aligned LLMs (Li &
Lee 2024a)
(Continued next page)
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(Continued from previous page)
Stage
Name
Level
Summary
SFT/DPO Mitigating Forgetting in LLM Su-
Low
Combining SFT and DPO sequen- tially leads to forgetting and a poor balance between goals (~ 2% MMLU).
3.2
pervised Fine-Tuning and Preference Learning (Fernando et al. 2024
Refine Large Language Model Fine- tuning via Instruction Vector (Jiang
High
Fine-tuning on TRACE shows declines primarily from lost instruction-following ability. Forgetting of domain knowledge, reasoning intensifies as model scale increases (~ 10% MMLU drop). Severity varies by architecture and pretraining quality; some models degrade sharply while others barely change.
SFT
3.3
et al. 2024)
An Empirical Study of Catastrophic Forgetting in LLMs During Continual
High
Fine-tuning (Luo et al. 2022)
Catastrophic Forgetting in LLMs: A
High
Comparative Analysis Across Lan-
guage Tasks (Haque 2025)
Mitigating Catastrophic Forgetting High in LLMS with Self-Synthesized Re- hearsal (Huang et al. 2024
Sequential fine-tuning, causes ma- jor forgetting; synthetic rehearsal mitigates it.
RL
Mitigating the Alignment Tax of Med RLHF (Lin 2024)
RLHF induces forgetting ("align- ment tax"); model averaging re- duces it.
3.2
SFT/RL
Understanding Catastrophic Forget- High ting in LLMs via Implicit Inference (Kotha et al. (024)
Fine-tuning skews the model's im- plicit task inference rather than eras- ing capabilities.
3.2
Temporal Sampling for Forgotten Rea-
High
Fine-tuned LLMs often forget so- lutions they previously generated ("temporal forgetting") across sizes and methods (SFT, GRPO).
soning in LLMS (Li al. 2025)
MEASURING SAMPLEWISE FORGETTING AND BACKWARD TRANSFER
To formalize these metrics, first consider an evaluation set of N multiple-choice questions with & options. For each sample let d aPth E {0, 1} indicate correct- ness before and after post-training. As illustrated in Fig. each sample falls into one of four quadrants task:
Backward Transfer
Retention
based the effect of training on
post
(i) Retention preserves knowledge (1+1),
(ii) Backward Transfer improves performance (0-11)
Non- acquisition
Forgetting
(iii) Forgetting reduces performance (1 0), and
(iv) non-acquisition has effect (0-
We define sample wise forgetting and backward trans- fer the proportions of 1 and 0-+1 flips, respec- tively:
0
(re
N
Figure 1: Each sample is assigned to one of four quadrants by correctness before and after.
1 1e
pre 1 IAa a. 0}
1=1
BT
1a 0 A a.
1}
N
i=1
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However, these intuitive metrics confound genuine knowledge change with label flips caused by guessing, especially when is small. For example, two independent random binary classifiers (k=2) yield F 0.25 because 0.5 x 0.5 0.25.
chance baseline for flips. To account for guessing, we assume simple response model: on each item the model either knows the answer or guesses uniformly among the choices. Let á be mean accuracy set. Then
a
Oirue
Ouee + 2, where x is the fraction correct by chance. Since an incorrect guess occurs with probability (k 1)/k,
Accuracy ā de- composes into true knowledge âque and lucky guesses
Figure 2:
1 0 +
k
k
1 1-+0 flip due purely chance requires (i) pre-training correct guess and (ii) post-training (converse for backward transfer). Assuming independence between pre- and post-training guessing events,
aro
F Channce
7
post
BT chance
1
k h-
incorrect (post)
incorrect
conrect by chance (pre)
conrect by chance (post)
These metrics depend only on aggregate accuracies and k; they require neither logits nor heavy computation.
Chance-adjusted forgetting and backward transfer. From these estimates we can isolate knowl- edge change beyond chance by subtracting the baselines from the respective forgetting/backward. transfer metrics and clipping at zero:
Irue max
Fehunce; 0),
BT true max BT BT chance; 0).
For example, if accuracy drops from 80% to 70% on 4-option MCQ test, raw forgetting is 10%. but chance-adjusted forgetting is only about 6% showing how the correction removes the effect of lucky guesses. Clipping ensures the metric remains valid even if models perform below chance. In practice, for an accurate measure forgetting, this metric's 's mean and variance statistics should be computed over multiple seeds, as described in Section B.1
Ceilings: how much could a model forget or improve? Observed forgetting can be small simply because little was truly correct to begin with. The maximum possible forgetting equals the fraction truly correct before post- post-training, which we adjust for guessing and clip at O:
F max
pre Grree
max
the ",T",,
max
k aFe
0)
Similarly, the maximum possible backward transfer equals the fraction truly correct after post-training:
BT max
Oure
max
posst 0) max
post
0)
where
Gpre
Apost
Pre
post
1
By construction Fime < F Fmax and BTime S BT Reporting the adjusted metrics alongside these ceilings separates true knowledge loss/acquisition from chance and contextualizes headroom for degradation or improvement.
Assumptions and scope. The correction uses two assumptions: (i) when the model does not know an answer, it guesses uniformly at random: and (ii) pre- and post-training guessing events independent. These assumptions allow dataset-level adjustments from pre- and post-training accuracies alone. Note that F Fire could quantify failure to elicit previously accessible knowledge and need imply that the model has lost/unlearned the underlying information. Likewise, changes in BT iree often reflect improved elicitation rather than newly acquired knowledge.
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3 WHEN, WHAT & How MUCH Is PRETRAINING KNOWLEDGE FORGOTTEN?
In this section, we ask three questions:
1. When is pretraining knowledge forgotten?
Our analysis spans four widely used continual-training regimes: (i) domain-continual training (3..), (ii) instruction tuning (232) (ii) light SFT/RL on reasoning traces, and (iv) large-scale SFT/RL for reasoning ( ($3.3) In total, we evaluate almost 30 model-training combinations reflecting common post-training practice, providing broad coverage of how contemporary LLMs are post-trained in the wild. Each post-trained model is compared with its initial checkpoint ($
2. What pretraining knowledge is forgotten?
We evaluate each model on 12 public benchmarks, collectively subdivided into close to 100 total subdomains. To summarize systematic patterns, we cluster sub-benchmarks into nine semantically coherent groups that exhibit similar forgetting trends (e.g., commonsense, culture, logic, language, liberal arts, science/tech). These clusters provide better map of which pretraining knowledge areas are most affected by given post-training recipe.
3. How much pretraining knowledge forgotten? Unless stated otherwise, chance-adjusted metrics for forgetting (Fine) and backward transfer (BT lrue) are used to quantify the severity.
Experimental setup. We standardize settings across models for fair comparison. All experiments use the LightEval framework (Habib et al. 2023) and log per-sample accuracy. We apply zero-shot chain-of- -ftthhughtt prompt to instruction-tunee models; base models receive a few-shot prompt solely to teach the format. When available, we add chat-specific templates to be in line with best practices. We cap sequence length 32,768 tokens except for Swen2.55-B-Mane 70--5--7--atthmme
and YYngg et al., (024b). which are limited to 4096. Because base models sometimes continue into subsequent questions, we set explicit stop sequences to end generation once prediction is produced. Decoding uses temperature 0.6 with nucleus sampling (top p) of 0.95. We provide additional details in the Appendix and extensive quantitative results in $ the following sections present figures and qualitative commentary, with moderate forgetting defined as 15 + 5%, low as below that threshold, and high as above To facilitate reproducibility and further inquiry, we release per-sample logs for every sub-benchmark alongside code.
We now present our results in the subsections below.
3.1 SUBAREA 1: DOMAIN-CONTINUAL PRETRAINING
Motivation. A popular class of continual learning works adapt general LLMs at the application layer for domains such as coding, mathematics, search, and tool use (Schick et al. 2023 Zhihong Shao Ma et al. 2023 As generalist LLMs are increasingly wrapped with tools and domain-specific interfaces, specialization must not erode broad pretraining knowledge. Models still need to contextu- alize domain outputs, communicate with diverse users, respect cultural norms (Pawar al. 2025). and uphold safety and ethical standards (Weidinger et al. 202T). These needs motivate our study of forgetting and backward transfer under continul pretraining.
2024
Setup. We study continual pretraining that converts a general base model into a specialized one, exemplified by Qwen2.5-Codee (Hui et al., 2024) and Qwen2.5-Math (Yang et al., (024h) [ Un- like general instruction tuning or reasoning post-training domain-continual pretraining shifts the underlying representation using large, relatively uncurated, web-scale domain corpora.
Main results. Figure 3 summarizes our findings. Domain-comtinual pretraining induces little to moderate amounts of forgetting among all post-training methods we evaluate. Backward trans- fer to general abilities is weak: Gains in the specialized domain rarely improve non-target tasks. The effect spans categories of pretraining knowledge, with no single category driving it, although specisppeialized models show significantly more forgetting. Lastly, larger models forget less and have marginally better backward transfer.
'This budget was sufficient in practice; we never required more tokens.
We treat domain-continul reasoning via SFT/RI. separately in 333 and focus domain-continual training
here.
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Common Sense
Common Sense
Sciencel Tech
Culture
Tech
Culture
Math (Gen)
Math (Gen)
Logic
Logic
20%i
40%%
60%
20%
4096
60%
Math
Knowledge
Math
Knowledge
Liberal Arts
Language
Liberal Arts
Language
Qwen 2.5 Coder (14B)
Qwen 2.5 Coder (32B)
Qwen 2.5 Coder (3B)
Qwen
(7B)
Qwen 2.5 (7B)
Figure 3: Forgetting (left) and Backward Transfer (right) after domain-continual pretraining. Forgetting is -o-oderraeee and consistent across categories; backward transfer is low. Scaling model size reduces forgetting.
Qualitative analysis. We performed manual error analysis, which indicates reduced instruction-followinn fidelity (e.g.. weaker adherence to constraints, formats, and role-specific di- rectives). Evidence of this found in supplemental tests using zero-shot chat-template evaluation. In this case, coder model may, for example, answer "Who the president of the US?" with response followed by code, often with embedded answers, making extraction of the true answer difficult. While few-shot prompting alleviates this, demonstrates weakened instruction-followingg ability and less easily elicited knowledge.
Takeaway
Domain-continun pretraining yields mow-to-moeerate forgetting across categories; backward transfer is limited. Scaling model size marginally reduces forgetting. This suggests that current domain-continual pretraining pipelines alleviate much of the large forgetting behavior seen in previous literature.
3.2 SUBAREA 2: INSTRUCTION TUNING
Motivation. Base models often require carefully engineered prompts elicit pretraining knowledge, limiting usability (Brown et al. 2020 Min et al. 2022). Modern post-training pipelines therefore add instruction tuning enable natural user interaction with minimal prompting (Ouyang al., 2022 Wei et al. 202Ib. Most continual-leamingg learning work we surveyed focuses on mitigating forgetting in this setting [Wu al. 2024). We ask: To what extent does instruction following come at the expense of previously learned knowledge?
Setup. We measure forgetting and backward transfer from instruction tuning in generalist models, Qwen2.5 Yang et al. 2024a) and Llama 3.1 (Dubey et al. 2024), as well as domain-continual pretrained models, Owen2.5-Coder (Hui al. 2024
Results. As shown in Figure 4 there low to moderate forgetting across models, with spikes in the Culture and Knowledge categories. However, there is substantial backward transfer in the Math category. Furthermore, scaling model size reduces forgetting and increases backward transfer. This effect is consistent continual learning literature focuses on reducing forgetting in this area, we note that forgetting is low moderate with current training practices.
domain-general and domain-specific base models. While most of the
'Qwen2.5-Math Instruct notably tuned with GRPO, leading classify it under Reasoning.

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Published as a conference paper at ICLR 2026
Common Sense
Common Sense
Sciencel Tech
Culture
Tech
Math (Gen)
Math (Gen)
Logic
Logic
40%
63%
Knowledge
Knowledge
Liberal Arts
Language
Liberal Arts
Language
Qwen 2.5 Instruct (14B)
Qwen 2.5 Instruct (328) --- Qwen 2,5 Instruct (3B)
Qwen 2.5 Instruct (7B)
Qwen 2.5 Coder Instruct (14B)
Qwen 2.5 Coder Instruct (32B)
Qwen 2.5 Coder Instruct (3B)
Qwen 2.5 Coder Instruct (7B)
Liama Instruct (8B)
Figure 4: Forgetting (left) and Backward Transfer (right) after instruction-tuning, Instruction tuning yields moderate forgetting and backward transfer across categories; scaling model size reduces both.
Qualitative analysis. Transfer gains likely reflect better elicitation of pretraining knowledge: instruction-tuned models can leverage what they already know using the straightforward prompts, whereas base models often require carefully crafted prompts.
Takeaway
Instruction tuning produces Now-to-moderate forgetting overall and moderate backward-transserr, particularly in math, across model families; the forgetting and backward transfer tend to decrease with increasing model scale. Shifting focus to other subareas of post-training might spur interesting research directions, but there still progress to be made in this area.
3.3 SUBAREA 3: TRAINING WITH REASONING TRACES (SFT AND RND
Motivation. Recent methods encourage explicit reasoning by letting models think scratchpad before answering (Wei et al. 2022; Kojima et al. 2022). which are now scaled in size and trace length with RL objectives (DeepSeek-A) Al 2025). As training domains and data grow, measure how much such reasoning training induces forgetting to guide continual- -ecaniinll practice.
(i) starting from base model and (ii) starting from instruction-une -unnee model. For the latter, we separate light-touch post-training (small datasets) from heavy post-training. We do not separate RL from SFT as the behaviors with respect forgetting and backward transfer are similar across the two objectives.
Setup. We consider two settings:
3.3. TRAINING WITH REASONING TRACES FROM BASE MODELS
Models. We evaluate QwQ-32B (from Qwen2.5-32B Base) (Qwen Team 2025). 1aan2255Matt-77--nnttree (RL post-trained with GRPO), and RieDstl models dis- tilled from different base models (Qwen2.5 Base and Llama 8B Base) (DeepSeck-Al 2025).
Results. From Figure across scales, model families, and training types, observe large backward- transfer gains, particularly Math and Logic, with minimal forgetting. Forgetting is generally low, but moderate for Knowledge and high for Culture. The exception this trend the Qwen2.5 Math Instruct model which shows substantial forgetting across many categories. Sample-wise inspection shows that this is primarily due weak adherence the prompt, sometimes outputting random

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Published as a conference paper at ICLR 2026
Sciencer Tech
Culture
Culture
Math (Ger)
Math
Logic
(Gen)
Logic
Math
Knowledge
Math
Knowfedge
Liberal Arta
Language
Liberal Arti
Languago
QuQ (326)
R1 Distil Qwen (326) -
Qwen 2.5 Math Instrrctt (7B)
Disiil
R1 Distil Liama (88)
Figure 5: Forgetting (left) and Backward Transfer (right) after reasoning training (SFT/RL) from base model. It generally yields minimal forgetting, except in the Culture and Knowledge categories, and has moderate to high backward-transfer gains. Qwen2.5 Math Instruct (7B) is an exception to this trend, demonstrating forgetting across all categories.
multilingual text. With this exception aside, compared instruction tuning the same base model (Figure 4). we see similar forgetting and larger backward transfer
We conclude that much of the backward transfer reflects improved instruction following. To isolate reasoning effects beyond elicitation, the next sections analyze reasoning training that starts from an instruction-fune model. However, models with light-touch reasoning training (i.e. low data) behave differently from those trained at scale (i.e. high data). We therefore present these two cases separately.
Takeaway
Training with SFT/RL for reasoning results in dynamics similar to instruction tuning, but to an even greater extent: We generally observe low to moderate forgetting overall and larger category-specific backward transfer gains. Forgetting mitigation in this domain should consider broad categories of knowledge and abilities when measuring forgetting and backward transfer.
3.3.2 REASONING TRAINING FROM INSTRUCTION-TUNED MODELS: LOW-DATA SCENARIO
Models. We the s1.1 family (7B, 14B, 32B) (Muennighoff al. 2025) and LIMO (vl and v2) Ye et al. 2025), all tuned from correspondingly sized Qwen instruct models.
Results. Figure 20 summarizes our findings. Across categories, models show minimal forgetting and low backward transfer, except in generative math, where large gains occur. This makes sense, as training for few passes little data leaves pretraining knowledge largely intact. That is, the model does not forget much, but it also exhibits few backward transfer gains beyond the instruction- baseline. Scaling model size marginally lowers forgetting, and the smaller similarly tends to reduce backward transfer, with the exception the Knowledge category.
gap
Takeaway
For the low-data regime, reasoning training from instruct models yields low forgetting and backward transfer. Forgetting decreases with model scale; backward transfer gains also tend to fall with narrowing student-teacher gap. This suggests that future forgetting mitigation literature on reasoning models should focus on medium-to- sized training datasets.
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Common Sense
Common Sense
Sciencel Tech
Sciencel Tech
Culture
Culture
Safety
Safety
Logic
edds
40%
6CH6
Pa 40%
60%)
Math
Knowledge
Math (Gen)
Knowledge
(Gen)
Math
Language
Math
Language
Liberal Arts
Liberal Arts
INTELLECT-2
Nemotron Code Reasoning (14B)
OpenThinker2 (32B)
Nemotron Code Reasoning (7B)
Thinkri (7B)
OpenThinker (7B)
Open Math (8B)
Figure 6: Forgetting (left) and Backward Transfer (right) after reasoning training from instruct: high data scenario. No single factor robustly explains the dynamics of forgetting and backward transfer.
3.3.3 REASONING TRAINING FROM INSTRUCTION-TTNED MODELS: HIGH-DATA SCENARIO
Models. We evaluate OpenCodeReasoner (Ahmad et al. 2025), OpenMath2 (Toshniwal et 2024), OpenThiiners
TrennThinner2-3215 and Open'Thnne Thinker3-7B (Guha al. 2025), and Intellect-2-32B (Prime Intellect Team et al. 2025). This spans SFT (former) and RI. (Intellect-2).
Results. Results vary by domain mix and model quality. The Open Thinker models generally show mowderte forgetting and moderate backward transfer, perhaps due the breadth of their training data mix, whereas OpenCode ReenSodeReasoner models show consistently high forgetting with low backward transfer gains due their narrower training data. Furthermore, we find this may be primarily due to weakened instruction- following capabilities, as sample-level inspection shows the model will refuse answer with letters when numbers are present as options, instead answering numerically. This is also seen with the Nemotron Code Reasoning models, where answers will often be embedded within python code. These factors can make the forgetting and backward transfer observed highly dependent on the extraction method used. We account for this through LLM-as-a-judge evaluation ($ C.4). Scaling model size, as seen the OpenThinker models, signals improvements in both forgetting and backward transfer - consistent with most previous sections. Decentralized training (as in Intellect-2), contrast, showed minimal forgetting or backward transfer. We conjecture that the model largely remains unchanged given that it shows negligible gains on the optimized math benchmarks [occlehnerr al. (2025). However, the results here remain preliminary. We not find a single dominant factor, whether initialization, data regime, scale, that sufficiently explains dynamics. We believe controlling the finer details that determine
-ortucctioo-ooooiinnn
forgetting and backward-tran the quality of the trained model might lead to better conclusions.
Takeaway
No single factor robustly explains the dynamics of forgetting and backward transfer; training on a mix of domains appears to reduce forgetting and improve backward transfer.
4 DOES MODEL MERGING REDUCE FORGETTING?
Motivation. Recent work shows that offline model merging can combine capabilities from multiple models (Dziadzio et al. 2025). Unlike classical continual learning (De Lange et al. 2022) model merging requires neither the original training data nor the ability to resume training, which is practical in conotrre-constraineed settings.
All corresponding tables available in L. for detailed comparison.
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Common Sense
Common Sense
Tech
Tech
Math
45%6
Logic
Math
Logic
Iberal
Knowledge
Liberal
Knowledge
Language
Language
Linear (0.2)
Linear (0.8) - Slerp
Slerp
Figure 7: Forgetting (left) and Backward Transfer (right) of Qwen 2.5 Base merged with Qwen 2.5 Coder (7B) relative to Qwen2.5 Coder. The merged model induces moderate forgetting and little backward transfer.
Setup. We evaluate Exponential Moving Average (EMA) merging; in the two-checkpoint case this is linear interpolation,
P (ax) = a pre + (1 (x) post-
Prior large-scale studies find these simple schemes effective for continual learning with founda- tion models (Roth et al. 2024). Our experiments compare linear interpolations (e.g., LERP and SLERP) across OpenThinker-788 OpenThinner3-77B, and Qwen2.5-Coder-778 together with their base checkpoints.
Results. We compare merged checkpoints to the post-trained model Ppoat: results for 8.re appear in the Appendix. For Qwen2.5-Coder and Open Thinker3-77 even small mixes with the base checkpoint degrade performance, severely for the latter (Figures 7 24). In contrast, OpenThinker-7B shows small overall gains, accompanied by moderate forgetting (Figure 28). In our setting, merging does not mitigate forgetting. This may reflect that merge only two checkpoints, whereas prior work often merges eight or more (Yadav et al., [223] 2024). We further hypothesize that weight drift between checkpoints is larger than typical for models in the merging literature, which could explain these outcomes.
Takeaway
Merging models does not yet reliably mitigate forgetting in post-training pipelines.
Merging remains promising, but further study is needed to how to overcome its limitations and whether increased scale can compensate for these difficulties.
CONCLUSION
We present a new metric for sample-wise forgetting and backward transfer that corrects for chance in multiple-choice evaluations. Our results challenge the claim that sequential training induces catastrophic forgetting of pretraining knowledge, showing this is not universally true. Forgetting depends the post-training method and its scale. By focusing sample-wise forgetting, offer a clearer map of what knowledge is lost and at what stages post-training language models lose knowledge, providing fertile ground to study how preserve and accumulate knowledge and capabilities by post-training. Promising ways to prevent forgetting include: (1) Designing objectives and data that explicitly penalize 1-+0 transitions; (2) Using targeted synthetic corpora brief mid- training bursts to repair localized forgetting; (3) Adding retrieval mechanisms to reduce reliance on in- weight knowledge storage.
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ACKNOWLEDGMENTI
AP and MB acknowledge financial support by Federal Ministry of Research, Technology and Space (BMFTR) FKZ: 161S24085B and Open Philanthropy Foundation funded by the Good Ventures Foundation. AH acknowledges funding by the Federal Ministry of Research, Technology and Space (BMFTR), FKZ: 16S224099.. AH thanks the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for support.
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Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.
Qihao Zhu Rumxin Xu Junxiao Song Mingchuan Zhang Y.K. Li Y. Wu Daya Guo Zhihong Shao, Peiyi Wang. Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024. URL httpt://t//: www. abs 2402. 03300
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Appendix
CONTENTS
G
Prompts
38
G.
Instruction- Prompt Template
38
Related Work
19
G.2
Base Model Prompt Template
38
Experimental Setup
20
G.3
LLM Extractor Template
38
B. I Evaluation
20
Dataset Groupings
39
B.2 Datasets
20
C Methodological Analysis
21
Tables
40
Prompting
21
L.1
Instruction Tuning
40
Sample vs.
Aggregate Metric
1.2
Pretraining
42
Comparison
22
Trained from Base
43
C.3 Metric Robustness under MCQA
23
1.4
Trained from Instruct High Data
Extraction
24
Scenario
Trained from Instruct Low Data
D Expanded Comparisons
25
Scenario
46
D.I Data Diversity
26
Qwen2.5 Base and Coder Merge (Relative to Qwen2.5 Base
D.2 Objective Function
27
47
D.3 Data Volume
29
Qwen2.5 Base and Coder Merge
(Relative to Qwen2.5 Coder)
48
Supplementary Plots
31
1.8
Qwen2.5 Instruct and Open- Thinker 7B Merge (Relative to
Reasoning Training From In-
Qwen2.5 Instruct)
48
struction: Low-Dala Scenario
31
1.9
Qwen2.5 Instruct and Open- Thinker 7B Merge (Relative to OpenThinker]
E.2
Maximum Forgetting
31
Model Merging Analysis
33
1.10
Qwen2.5 Instruct and Open- Thinker3 7B Merge (Relative to
Weight Drift
33
Qwen2.5 Instruct)
50
F2
Failure Case: OpenThinker? Thinker3
34
Owen2.5 Instruct and Open- Thinker3 7B Merge (Relative to
Failure Case: Coder Models
35
Moderate Case: Thpekehinner
36
50
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A RELATED WORK
Post-training techniques. A broad set of post-training methods now underpins standard LLM pipelines. Supervised fine-tuning (SFT) (Ouyang et al., 2022) remains the core step, used for continued pretraining and instruction tuning. At later stages, reinforcement learning from human feedback (RLHF) (uyang et al. 2022) aligns model outputs with human preferences. To simplify preference learning, direct preference optimization (DPO) (Rafailov et al. 2023) provides a direct loss surrogate. With the rise of test-time scaling (e.g., sampling depth or compute at inference), group relative policy optimization (GRPO) (Shao et al., 2024) has been proposed to elicit stronger intrinsic reasoning. Taken together. these methods introduce distinct objectives and optimizers, increasing the complexity of the post-training stack (Wang et al. 2025).
Measuring catastrophic forgetting. Catastrophic forgetting is the loss of previously acquired knowledge when a network learns new information. Early studies examined the effect in small models and simplified settings (McCloskey & Cohen 1989; Ratcliff 1990; French] 1999). Lopez- Paz & Ranzato (017)) formalized forgetting via backward transfer, the effect of learning a new task on performance in earlier ones: positive values indicate improvement; negative values indicate forgetting.
Recent work extends these analyses to deep networks trained on large-scale data, with growing attention to language models (Bicsialska et al. 2020 Wu et al.) (0022).
Benchmark paradigm. Task-incremental learning is the dominant paradigm for benchmarking forgetting (De Lange al.) (022). Models learn a sequence of tasks with clear boundaries, and task labels are available at train and test time. Class- incremental learning removes test-time task identifiers, making evaluation stricter (Wang et al.) 2024). Other views analyze continual learning through positive/negative transfer (Yildız et al. (0255). At the sample level. Toneva et al. (2019) introduced forgetting metrics that identify "unforgettable" examples (stable once learned) and "catastrophically forgotten" " examples (highly plastic), and showed these patterns are consistent across architectures and random seeds.
Language-model forgetting. Recent studies focus on forgetting induced by instruction tuning. Luo et al. (2025) trained models of up to 7B parameters with SFT and evaluated multiple knowledge categories. DeepSeek-Al (2024) reported instruction tuning-related regressions on sentence com- pletion even for 67B models. Fernando et al. (2025) examined forgetting across SFT followed by strategies to mitigate it. Lin et al. (2024) framed instruction-tuning degradation an "alignment tax" (performance loss on pretraining skills due to alignment) and found model merging to be the most effective mitigation under a Pareto-efficient trade-off among tested techniques. [i & Lee (202bb) studied continual pretraining aligned LMs and observed notable regressions in alignment-related behavior.
instruction-tuning-eele
RLHF and proposed
Catastrophic forgetting in reasoning training pipelines. Work on reasoning oriented LMs high- lights new failure modes. et et al. (2025) defined temporal forgetting: forgeral models lose the ability to solve problems they could solve at earlier training checkpoints. The effect appears in both RL-trained and instruction-tuned models. They proposed temporal sampling-rounddnieg round-rooinn sampling from recent checkpoints- as mitigation. [Pipatanakul et al. (2025) merged a language-fine fine-tuned model with DeepSeek R1 Distill (70B; both derived from Llama 3.3 70B (Dubey et al. 2024 to adapt reasoning while preserving language competence. For multimodal models, Chen al. (2025) found that later layers primarily support reasoning, whereas early layers handle perception, suggesting layer wise interventions. We extensively document forgetting across post-training pipelines.
Mitigation strategies. Sequential SFT to RLHF/DPO can exacerbate forgetting. To counteract this, researchers explore: (i) model averaging, interpolating between pre- and Rost-RL.HF checkpoints to trade off alignment and retention (Lin et al., 2024); (ii) joint post-training, optimizing supervised and preference objectives simultaneously with convergence guarantees (Fernando al.) (024); and (iii) unified fine- -ttiin ( (UFT), which folds instruction tuning and alignment into a single implicit-reward objective (Wang et al. 2025). Additional techniques iinluuding advantage models and selective rehearsal- stabilize RLHF by shaping reward distributions and replaying curated data (Peng et al.) 2023). Online Merging Optimizers (OMO) combine gradients from SFT and RLHF models during
training to maximize reward while preserving pre-trained skills (Lu et al. 2024). Theory supports these interventions: up permutation symmetries, weights of homologous models tend to lie in
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shared low-loss basin (Ainsworth et al. 2023). Hence, quite surprised that model merging does not work for our simple case of mitigating forgetting during post- post-training with only two deep networks.
Forgetting at scale. Pretraining mitigates forgetting relative to training from scratch (Mehta et al. 2023 McRae & Hetherington [193). Ramasesh et al. (2022) further found that pretrained ResNets and Transformers (up to ~ 100M parameters) are robust to forgetting at scale; language experiments showed similar trends. However, Luo et al. (2025) reported increased forgetting with scale in the 1-7B LM regime, suggesting modality- and regime-dependent behavior. In contrast to these works, we study forgetting during post-training of language models.
B EXPERIMENTAL SETUP
B.1 EVALUATION
We standardize settings across models for fair comparison. All experiments use the Li rightEvaa framework (Habib al. 2023) and save sample-wise results, such accuracies. We apply zero-shot chain-of-thought prompt to instruction-tuned models and require answers in a fixed MCQ format; base models receive few-shot prompt solely to teach the format. When available, we add chat-specific templates to be in line with best practices. We cap sequence length at 32,768 tokens, except for (Yang et al., 2024b), which are limited to 4,096 tokens. Decoding uses temperature 0.6 with nucleus sampling (top-p) of 0.95. All datasets are evaluated on least 3 seeds and the metrics are reported with mean and standard deviation ($ To facilitate reproducibility and further inquiry, we release per-sample logs for every sub-benchmark alongside code.
Qwen2.5-7B-Mathh and Owee2.5-77-Math--Instee
Chat-specific templates are incorporated into the prompt to ensure consistent formatting and fair evaluation. Because some models, particularly base models, tend to continue generating responses for subsequent questions after completing the current one, we provide explicit stop sequences to terminate generation
prediction has been produced. For the case of base models, few-shot prompting yields a more accurate elicitation of their knowledge, as otherwise the model can output the answer in undesired formats ($ G.2
To encourage strict MCQA formatted answers for instruction-tuned models we prepend an instruction prompt with explicit answer formatting guidelines with an example ($G.I). This is necessary for accurate evaluation since models sometimes output the option text or phrases instead of the letter. In particular, we find including additional instructions not extra punctuation, asterisks, lowercase letters for answers, or trailing spaces necessary because LightEval's 's default regex and letter extractors can fail in certain cases otherwise. We additionally tell the model constrain its output to be of the form "Answer: SLETTER" as otherwise models will often provide the corresponding answer to a given letter or provide the answer in another format, making extraction more prone error.
For datasets where CoT reasoning traces are provided for few-shot prompting, we use those. For free- form/ Torm/generative math questions, we follow the prompt and extraction methods used in Hochlehnert ct al. 2025).
B.2 DATASETS
To evaluate broad model knowledge and capabilities benchmark eighteen public datasets:
MMLU (Hendryeks et al. 202 a), BBH (Suzgun et al., (023), GPQA (Rein et al. 2024). MuSR
(Sprague et al. 2024), ARC (Clrk al. 2018). TruthfulQA (Lin al.
2022h. HellaSwag (Zellers)
ct al. 2019). Social IQa (Sap et al. 2. MCTest (Richardson et al.
2013). PIQA (Bisk et al. 2024). AIME24 (AI-MO
2020). CA (Talmor et al. 2019). SaladBench (Li al.
2024), AIME25 [Yen-Time Ting 2025), AMC23 (Engincering 2025 Math500 (HuggingFaceH4] (024)) Minerva (Engineering 2025b). and OlympiadBench (He al. 2024h. Several of these benchmarks, namely MMLU and BBH, provide subcategory labels which allow for splitting into further sub- benchmark evaluations by subject. To enable easier understanding, we group these nan-)aannchmark into high-level groups used to evaluate the capabilities of the models. They are grouped such that (sub-)beence buub))benehmarks in the same group show similar trends in forgetting and backward transfer (SH).
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C METHODOLOGICAL ANALYSIS
Evaluating a wide range of models and datasets requires careful attention to the form of prompting. metric comparison, metric robustness, and extraction accuracy. We check each of these categories to verify assumptions and determine the way to conduct experiments that most accurately measures true the change of knowledge in models under post-training.
C.I PROMPTING
When evaluating base models, prompting, in particular, becomes an important factor to consider in assessing the correctness. Therefore, first measure the ability of base models using the same prompting as instruction-turg tuned models. Under these conditions, we see ostensibly large forgetting in domain-comtinual pretrained models (Figure 8) Our qualitative analysis suggests that this largely due to the models output in an incorrect format, such as in code. where the location of the answer can be obscured. When contrasted with few-shot prompting, where there is much less forgetting. we conclude that forgetting metrics can vary significantly depending on how knowledge is elicited, especially when training on narrow tasks, effect that few-shot prompting alleviates. Therefore. in all experiments, unless otherwise stated, we evaluate base models with few-shot prompting. it elicits the learned knowledge better. For these reasons, measuring the performance of base
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Figure 8: Forgetting (left) and Backward Transfer (right) of -omain-AAaptive Pretraining models evaluated with zero-shot chat template prompting instead of few-shot prompting. Substantial specious forgetting is observed relative to Figure 3 motivating our use few-shot prompting for all base model evaluations.
models in behavioral evaluations can become nontrivial. While benchmarks measuring knowledge or capabilities may be elicited through few-shot prompt, others, such as truthfulness or safety, become more difficult because prompting them with examples would bias their behavior (L.in et al.) 2022). Future work should consider the effect of no-knowledge few-shot prompting, where the question-and-ansee format is demonstrated without leaking content examples, to avoid biasing the base model's output.
Evaluation Decisions
Base models evaluated with few-shot prompting. Safety evaluations only conducted when comparing two instruction -unned models.
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C.2 SAMPLE VS. AGGREGATE METRIC COMPARISON
Another key consideration is empirical evidence demonstrating that sample-wise metrics uncover more forgetting and backward transfer than aggregate metries. A theoretical argument in favor of sample-wise metrics over the standard aggregate metric, defined as
Fstandard max
aPo0i 0)
has indicated this to be possible, but it unclear priori the degree which this occurs in practice. We confirm this to play a large role in uncovering forgetting in Figure Our sample-based metric reveals substantially more forgetring relative the standard formulation, some cases finding what was originally low forgetting is actually moderate. This highlights sample-level degradation that is otherwise hidden when averaging conclude that post-training preserved prior capabilities intact, yet this apparent stability can mask substantial changes. Simultaneous forgetting and backward transfer that cancel in expectation but reflect fundamentally different model. In domains such as legal or medical reasoning, where practitioners may rely model's established behavior for specific query types, such hidden redistribution of competence poses real deployment risks that aggregate metrics fail to flag.
tasks. A practitioner observing Fsamdard R 0 might reasonably
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Figure 9: Coder model comparing conventional forgetting (left) against our sample-wise forget- ting (right). More (genuine) forgetting is uncovered when using the sample-wise forgetting metric.
Evaluation Decisions
Sample-wise metrics are used over standard aggregate metrics.
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C.3
METRIC ROBUSTNESS UNDER MCQA
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Figure 10: Sub-benchmark count across 2-Seed Agreement Forgetting (x-axis) and Chance- Adjusted Forgetting (y-axis). The line of best fit (red) shows both metrics are highly correlated across sub-benchmarks.
We review the robustness of the chance adjusted forgetting measuring true knowledge loss, which is particularly relevant when evaluating under MCQA benchmarks as models can often guess the answer correctly. While our metric accounts for this by subtracting out an estimate of this probability, we compare this to another sample-leve metric which filters out noisy samples to empirically demonstrate this. Namely, we consider samples where there is agreement among two of three seeds on average, and consider the cases where there is forgetting relative to the other cases.
(1 (1)
= (0-0)2 + (0-> 1)2 + (1 -02 + (1-1)2
where we formally define the two-seed sample-agreement metric as follows:
N
(x (x~r)2 = (1s)
Σ
Σ
l pre = x. = pre A
pust
= = 0,
post
(a)))((
where S is the set of seeds and N the number of samples. Intuitively, this measures robust knowledge loss relative to stable knowledge. We find that this metric agrees with our results across the post- training pipeline, thereby indicating the chance-adjusted forgetting metric captures robust knowledge loss, rather than random forgetting. We show the correlation between these metrics in Figure I0 and provide direct comparison Figure II
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Figure 11: Seed Forgetting (left) and Chance Adjusted Forgetting (right) Models Trained from Base. The overall trends remain the same, with only minor differences such slight increase in forgetting for Qwen2.5 Math Instruct (7B) in the 2 seed forgetting case.
Evaluation Decisions
Chance-adjusted (sample-wise) wise) metrics are used MCQA benchmarks.
C.4 EXTRACTION
Sample-level inspection occasionally shows answers that correct, but unable to be extracted correctly through the regex extraction. This occurs particularly in models trained for specialized tasks. For example, find that coding models will assign the correct answer to variable in code and then provide the answer variable in a print statement. We control for these extraction-relate related errors by using LLM extractor, specifically Qwen2.5- 14B- 4-s-uuutuc,, which we find sufficient to correct for errors. We do this by providing the question, response, and ground truth answers using the prompt in G.3 without chat template, in order encourage immediate JSON output.
find that this primarily corrects for outliers while all trends remain the same, which we find to be true across all knowledge and post-training categories. By comparing Figure [2] where LLM extraction is used, and Figure 13 where regex extraction used, we see the outlier effect of Qwen2.5 Math Instruct (7B) is reduced. The overall trends remain the same.
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Figure 12: Forgetting (left) and Backward Transfer (right) Models Trained from Base using LLM Extraction. Trends are the same in Figure 13 but Qwen2.5-Math Instructtss outlier tendencies are reduced.
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Figure 13: Forgetting (left) and Backward Transfer (right) of Models Trained from Base using Regex Extraction. Trends are the same as Figure I2 but Qwen2.5-Math- Instrutsss outlier tendencies increased.
D EXPANDED COMPARISONS
The high-data reasoning training scenario in $3..33 encompasses heterogeneous collection of models that differ along several dimensions simultaneously: data diversity, training objective (SFT vs. RL), and data volume. To isolate the contribution each factor, this section disentangles these variables by grouping models according single axis time.
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D.1 DATA DIVERSITY
We split reasoning models into two cases: those trained on narrow domains (one or two benchmark categories), such as math code, and those trained mixed data (jointly trained many tasks or on general data). As indicated $3...3 increased data diversity generally mitigates forgetting and improves backward transfer (Figure 15 whereas decreased data diversity shows the opposite trend [Figure[4] This pattern consistent with the broader continual-learning intuition that rehearsing wider range of tasks reduces interference. 'hat is, models trained mixed data encounter the relevant knowledge distribution more often during training, reducing the probability of overwriting
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Figure 14: Forgetting (left) and Backward Transfer (Right) of Reasoning Models Trained with Narrow Data. Backward transfer is generally low or moderate and forgetting is larger than training on mixed data.
The natrow-data models in Figure 14 demonstrate moderate to high forgetting across most knowledge categories, while backward transfer remains low. This pattern is most pronounced in the Culture, Liberal Arts, and General Knowledge categories, which are structurally furthest from the narrow training distributions of math and code. This is consistent with gradient signals concentrated in a single domain, progressively displacing representations shared with general knowledge (French) 1999). Because narrow-domain training rarely revisits these -nneral-nnowedge representations, there no countervailing signal to preserve them. The result is capability gains in the target domain alongside losses in breadth.
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INTELLECT-2
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Figure 15: Forgetting (left) and Backward Transfer (Right) of Reasoning Models Trained with Mixed Data. Backward transfer is generally moderate to high.
Takeaway
Data diversity tends mitigate forgetting and improve backward transfer.
D.2 OBJECTIVE FUNCTION
Reasoning models are trained with variety of objectives: supervised fine-tuning (SFT) curated reasoning traces, reinforcement learning (RL) via policy-gradient methods such as GRPO, or a combination of both. In principle, SFT and RL could differ in their forgetting dynamics. SFT directly optimizes a cross-entropy loss over fixed demonstrations, while RL explores a broader policy space and updates shaped by reward signals that may be more less correlated with pretraining knowledge. However, in practice, Figures 16 and d7 not show systematic differences between the two objective types in the models evaluated. Both groups exhibit comparable levels of forgetting and backward transfer, suggesting that the training objective is less important than data composition and volume in determining forgetting behavior.
One potential explanation for this result is that both objectives operate on similar data. For example, an RL policy trained on math problems explores the same knowledge space SFT model trained on curated math traces, regardless of whether updates are shaped by cross-entropy or a scalar reward. Therefore, key variable could be the coverage of the training distribution, not the loss function. This interpretation supported by the heterogeneity within each group; models differ base checkpoint, data volume, and domain mix yet still converge comparable forgetting and backward transfer profiles.
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Figure 16: Forgetting (left) and Backward Transfer (Right) of Reasoning Models Trained with SFT Data.
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Figure 17: Forgetting (left) and Backward Transfer (Right) of Reasoning Models Trained with RL Data.
Takeaway
Differences between SFT and RL reasoning post-training in forgetting and backward transfer dynamics appear be negligible in practice.
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D.3 DATA VOLUME
Data volume is another important factor, since more training examples give the optimizer more signal to update weights, which could either help. by covering more knowledge, harm, by overwriting more pre-trained knowledge. Figure 18 shows that models trained on low data volumes exhibit uniformly low forgetting and backward transfer, consistent with the finding in $3.3 that light-touch training leaves pretraining knowledge largely intact. However, for high data volumes (Figure 19). no clear trend emerges. Forgetting and backward transfer vary substantially across models, suggesting that volume per se is not a reliable predictor once data abundant. At that point, the composition of training data appears dominate.
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Figure 18: Forgetting (left) and Backward Transfer (Right) of Reasoning Models Trained with Low Data Volume. Both metrics are generally low across categories.
The low-volume radar plot in Figure [8 is notably uniform. This uniformity is itself informative. It implies that a carefully-conducted, small training signal insufficient meaningfully reshape large categories of pretraining knowledge, regardless of domain. From a continual-learning perspective, low-volume training may occupy regime where the optimizer takes steps small accumulate into large weight displacements, leaving the pretrained basin essentially intact.
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Figure 19: Forgetting (left) and Backward Transfer (Right) of Reasoning Models Trained with High Data Volume. Forgetting and backward transfer vary significantly between models.
Takeaway
Low data volume tends to mitigate forgetting.
Taken together, the three ablations in this section point toward data composition as the primary driver of forgetting in reasoning post-training. Data diversity and data volume both show clear monotonic relationships with forgetting, whereas the training objective does not. This ordering has practical implication. Practitioners seeking reduce forgetting without sacrificing reasoning gains should first consider the breadth and quantity of their training mix before redesigning the loss function. Specifically, mixing in general-domain data may substantially reduce category-specine forgetting moderate cost to in-domain performance.
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E SUPPLEMENTARY PLOTS
E. REASONING TRAINING FROM INSTRUCTION: LOW-DATA SCENARIO
Figure 20 shows the full radar charts for the low-data reasoning training scenario described in $3.3 Across all models and categories, both forgetting and backward transfer are low, confirming that light-touch post-training leaves most pretraining knowledge intact. The s1.1 model family (7B, 14B, 32B) shows a consistent decrease in forgetting with scale. Backward transfer gains concentrate almost exclusively in the Math (Generative) category, reflecting the narrow training distribution, with negligible change elsewhere. LIMO models exhibit a similar pattern: backward transfer is confined generative math and minimal disruption occurs in other knowledge categories.
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Forgetting (left) and Backward Transfer (right) after reasoning training from instruct: low data scenario. Yields little forgetting and backward transfer, with the exception the Math (Gen) category. Forgetting decreases with model scale.
Figure 20:
E.2 MAXIMUM FORGETTING
We address another potential confounder in our results. Namely, a post-trained model's maximum forgetting (backward transfer) is bounded by its base model (i.e., the model before post-training): if the base model never learned capability, the post-trained model cannot forget it further. This means that models with stronger base capabilities are inherently exposed to greater forgetting risk, which could confound comparisons across model families. To assess whether this bound is limiting factor in our analysis, we compare empirical forgetting against the theoretical maximum for each model. Figure 21 and Figure 22 shows that this does play significant role in our analysis: in practice, the gap between empirical and maximum forgetting is large across all evaluated models, indicating that post-training does not push models close their forgetting ceiling. Our conclusions are therefore unlikely to be an artifact of this constraint. Further results are shown in $
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Logic
20%
40
50% go
Liberal Arts
Knowledge
Liberal Arts
Knowledge
Language
Language
Qwen 2.5 Instruct (14B)
Qwen 2.5 Instruct (328)
Qwen 2.5 Instruct (3B)
Qwen 2.5 Instruct
Qwen Coder Instruct (148)
Qwen Coder Instruct (32B)
Owen Coder Instruct (3B)
Qwen 2.5 Coder Instruct (7B)
Llama 3.1 Instruct (8B)
Figure 21: Forgetting (left) and Backward Transfer (right) after instruction-tuningg Dotted (solid) lines represent maximum (empirical) values.
Common
Common Sense
Sense
Sciencel Tech
Sciencel
Culture
Tech
Culture
Safety
Logic
Safety
20%
40%
604
Math
Knowledge
Knowledge
Liberal Arts
Language
Liberal Arts
Language
s1.1 (14B)
LIMO
LIMO v2 - s1.1 (328)
s1.1 (7B)
Figure 22: Forgetting (left) and Backward Transfer (right) after reasoning training from instruct: low data scenario. Dotted (solid) lines represent maximum (empirical) values.
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F MODEL MERGING ANALYSIS
This section provides full radar charts and qualitative analysis for the model merging experiments. We evaluate linear interpolation between post-trained and base (or instruct) checkpoints, examining three cases: two outright failures (OpenThinker3 and Qwen 2.5 Coder) and one moderate success (OpenThinker). The weight-drift analysis in oF.Tes unifying explanation for why merging succeeds or fails.
F.1 WEIGIT DRIFT
We observe large weight drift among models in which merging fails. Specifically in the case model trained from instruction tuned bases, we compute the ratio of the L2 norm of the task vector (from the model to the instruct model) the L2 norm the base model. In the case of OpenThinker3 this is just above 20%. Likewise Qwen2.5 Coder (7B) has value of 87%. OpenThinker and s1.1, which we find are both mergeable, have values of only 1.8% and 0.6%. respectively.
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F.2 FAILURE CASE: OPENTHINKER3
OpenThinker3 7B reasoning model derived from Qwen 2.5 Instruct via large-scale RL, post- training. Despite sharing the same base, its task vector has L2 norm ratio of approximately 20% relative to the base model, roughly ten times that of the mergeable OpenThinker (F.) Figures 23 and 24 evaluate the merged checkpoint relative Qwen 2.5 Instruct and OpenThinker3s itself, respectively. Both evaluations reveal large forgetting across all categories. Sample-level inspection shows the merged model frequently degenerates into repetitive output, looping over words, phrases, or numerical sequences without producing a coherent final answer. This behavioral collapse, in contrast to gradual redistribution of capabilities, suggests that the large task vector overwhelms the interpolation, displacing the checkpoint into a region of the weight space that supports neither model's 's behavior.
Logic
Logic
Sciencef
Science/
Tech
Knowledge
Tech
Knowledge
15%6
00%
45%
15%
30%
45%
Safety
Liberal Arts
Safety
Liberal
Arts
Math
Math
Linear (0.2)
Linear (0.8)
Figure 23: Forgetting (left) and Backward Transfer (right) of Qwen 2.5 Instruct merged with OpenThinker3 (7B) relative to Qwen 2.5 Instruct MMLU. Large forgetting occurs. Sample- level analysis shows the model output degeneration, with the model often repeating words or phrases, typically without providing final answer.
Figure 2 shows that the degradation is not confined to single knowledge cluster, but occurs broadly, spanning commonsense, culture, language, and liberal arts knowledge categories. This broad degradation is distinct from ordinary domain forgetting, in which one would expect forgetting to concentrate in areas semantically distant from the training domain. Instead, the merged checkpoint's failure is structural. Selective forgetting suggests a poor weight selection, whereas output collapse suggests the merged weights have landed outside the basin of any well-defined behavior. Figure 24 confirms the same pattern when evaluated from the perspective of OpenThinker to the merged model.
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Logle
Logic
Sciencel Tech
Sciencel
Knowledge
Tech
Knowledge
Safety
Liberal
Safety
Liberal
Arts
Arts
Math
Linear (0.2)
Linear (0.8)
Figure 24: Forgetting (left) and Backward Transfer (right) of Qwen 2.5 Instruct merged with OpenThinker3 (7B) relative to OpenThinker3 on MMLU. Large forgetting occurs. Sample-level analysis shows the model output degeneration, with the model often repeating words phrases, typically without providing a final answer.
F.3 FAILURE CASE: CODER MODELS
Qwen 2.5 Coder (7B) presents the most extreme weight drift our study. Its task vector has an L2 norm ratio of 87% relative to the base model, nearly equal in magnitude to the base weights themselves. This reflects the depth of domain-continual pretraining. The model has been substantially reshaped from Qwen 2.5 Base to specialize in code generation. Merging it back with the base therefore requires interpolating between two very dissimilar weight configurations.
Figures 25 and 26 show the result relative Qwen 2.5 Base and to Qwen 2.5 Coder, respectively. Both comparisons show moderate-to-larg. forgetting with low backward transfer throughout, The merge fails to consolidate the two checkpoints; the resulting model neither retains the base model's broad knowledge nor preserves the coder's specialized representations. Instead it occupies degraded intermediate state, illustrating that extreme weight drift makes interpolation destructive rather than complementary.
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Common Sense
Common Sense
Sciencel Tech
Science! Tech
Culture
Culture
Logic
Math
Logic
2500
30%
45%
15%
30%
45%
Liberal
Knowledge
Liberal Arts
Knowledge
Arts
Language
Language
Linear (0.2)
Linear (0.8)
Slerp (0.2)
Slerp (0.8)
Figure 25: Forgetting (left) and Backward Transfer (right) of Qwen 2.5 Base merged with Qwen 2.5 Coder (7B) relative to Qwen 2.5 Base all Benchmarks. Moderate-to--arge forgetting occurs with low backward transfer.
Common
Common
Sense
Sense
Sciencel Tech
Science! Tech
Culture
Culture
Logic
Logic
E996
30%
45%
5%%
30%
45%
Liberal
Knowledge
Liberal Arts
Knowledge
Arts
Language
Language
Linear (0.2)
Linear (0.8)
Slerp (0.2)
Slerp (0.8)
Figure 26: Forgetting (left) and Backward Transfer (right) Qwen 2.5 Base merged with Qwen 2.5 Coder (7B) relative to Qwen 2.5 Coder all Benchmarks. Moderate-to- forgetting occurs with low-to-moderate backward transfer.
F.4 MODERATE CASE: OPENTHINKER
OpenThinker (7B) provides a contrasting case to two failure cases above. Its task vector La norm ratio is only 1.8%, indicating that SFT on reasoning traces made comparatively minor weight adjustments relative to Qwen 2.5 Instruct. With much smaller weight drift, the two checkpoints are more geometrically compatible, and interpolation is less disruptive.
Figures 27 and 28 show the results relative to Qwen 2.5 Instruct and to OpenThinker, respectively. Both evaluations show only modest forgetting, and the Linear (0.8) interpolation yields marginal
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overall improvement relative to either endpoint. This is consistent with the weight- drift hypothesis, that models small changes from their initialization are more amenable to interpolation.
Logic
Logic
Sciencel Tech
Science/
Knowledge
Tech
Knowledge
15%
30%
45%
19%
30%
49%)
Safety
Liberal Arts
Safety
Liberal
Arts
Math
Math
Linear (0.2)
Linear (0.8)
Figure 27: Forgetting (left) and Backward Transfer (right) of Qwen 2.5 Instruct merged with OpenThinker Merge (7B) relative to Qwen 2.5 Instruct MMLU. We marginal overall performance improvement in the case of Linear (0.8).
Logic
Logic
Sciencel
Sciencel
Tech
Knowledge
Tech
Knowledge
Safety
Liberal
Safety
Liberal
Arts
Arts
Linear (0.2)
Linear (0.8)
Figure 28: Forgetting (left) and Backward Transfer (right) of Qwen 2.5 Instruct merged with OpenThinker (7B) relative to OpenThinker on MMLU, We see marginal overall performance improvement in both cases.
Figures 27 and 28 reveal that even in the moderate-sucest success case, the improvement is concentrated in the Linear (0.8) interpolation and is small in absolute terms. Heavier interpolation toward the base checkpoint progressively erodes reasoning gains without recovering broad knowledge proportionally.
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G PROMPTS
This section provides the exact prompt templates used throughout the evaluation pipeline. The instruction-tunek template ($G.1) is used for models that have undergone instruction tuning; the few-shot base template ($..2) is used for base models that require in-context demonstrations; and the LLM extractor template (G.3) is used to parse free-form model outputs account for regex-based
extraction fails.
G.1 INSTRUCTION-TUNED PROMPT TEMPLATE
(Instruction)
On the very last line, write exactly "Answer: SLETTER" (e.g. "Answer: B"), with no extra punctuation, no lowercase, no and no trailing spaces.
Think step by step, showing your reasoning. Question: "(Question)"
G.2 BASE MODEL PROMPT TEMPLATE
(Instraction)
Question: "(Few-Shot Question 1)" Reasoning: (how Reasoning Trace 1)
Answer: (Few-shot Answer 1)
examples
Question: "(Question)" Reasoning:
G.3 LLM EXTRACTOR TEMPLATE
You are strict extractor.
Given the FULL_PROMP (the original prompt to the model), the model output SNIPPET (last part, quoted), , and the gold extraction (quoted), return ONLY valid JSON with exactly two keys:
""extraction;; the final answer token (e.g. "A", "c", "42") or null if unknown
string
"correct"; true if the extraction matches the gold, false if it does not, or null if unknown
Do NOT output anything else (no explanation, code fences).
FULL PROMPT: {q promptt SNIPPET: iq_snippet)
GOLD: (g_gold)
Return JSON now:
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H DATASET GROUPINGS
The following taxonomy groups the evaluated benchmarks into nine thematic categories. These groupings the backbone for the forgetting and backward transfer radar charts throughout the paper
Commonsense
Math
Commonsense QA
BBH (geometric shapes, and boolcan expressions)
PIQA
MMLU (high school statistics. high school math- ematics, formal logic, elementary mathematics, cconometries, college mathematics, and abstract algebra)
Culture
BBH (sports understanding, movie recommenda- tion)
Math (Generative)
Logic
AIME24, AIME25, AMC23
Math500, Minerva, OlympiadBench
BBH (navigate, causal judgment, penguins in a
table, web of lies, tracking shuffled objects three
Safety
objects, tracking shuffled objects seven objects,
tracking shuffled objects five objects, temporal quences, reasoning about colored objects, logical deduction three objects, logical deduction seven ob-
MMLU (moral scenarios, moral disputes.jurispna
dence, and business ethics)
TruthfulQA (mcl)
jects, logical deduction five objects, formal fallacies,
SaladBench (mrq)
and date understanding)
ARC (easy and challenge)
Science & Tech
MuSR (murder mysteries, object placements, team
allocation)
(marketing, virology, professional medicine, professional accounting, nutrition, med- ical genetics, machine learning, human sexuality, human aging, high school physies, high school computer science, high school chemistry, high school biology. electrical engineering, conceptual physics, computer security, college physics, col- lege medicine, college computer science, college chemistry. college biology. clinical knowledge. astronomy, and anatomy)
MMLU
MMLU (logical fallacies)
Knowledge
BBH (object counting)
MMLU (miscellanenus,, global facts)
MCTest
Language
GPQA (diamond)
BBH (snarks, disambiguation qa, ruin names, hy-
perbaton, translation error detection)
Liberal Arts
Social IQa, Hellaswag
MMLU (world religions, us foreign policy, sociol- ogy, security studies, public relations, professional psychology, professional law, prehistory. philoso- phy, management, international law. high school world history, high school us history, high school psychology. high school microecomomics. high school macroeconomics. high school government and politics, high school geography, and high school curopcan history)
'MMLU evaluated with few-shot, no CoT prompting for base models. 'These are only used in comparisons that do not include base model because TruthfulQA and SaladBench are designed to measure the default behavior of the model rather than knowledge, which few-shot prompting would bias.
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I
TABLES
The following tables provide numerical counterparts to the radar charts in the main paper and in $D covering all post-training conditions and model families evaluated. Each cell reports chance-adjusted forgetting (or backward transfer) as percentage, the standard deviation across seeds after "+", and the maximum possible value in parentheses. For example, 12.3 10.4 (67.8) denotes 12.3% forgetting with standard deviation 0.4% and a ceiling of 67. 67.8%. Forgetting and backward transfer tables are paired within each subsection. The first five subsections cover the post-training regimes analyzed in 3 the remaining ones provide numerical detail for the merging experiments discussed in $3]and
1.1 INSTRUCTION TUNING
Numerical results corresponding to Figure 4 covering Qwen 2.5 Instruct (3B-3) (3B-32B), Qwen 2.5 Coder Instruct (3B-32B), and Llama 3.1 Instruct (8B).
Table 2: Instruction Tuning: Forgetting (Part 1 of 3)
Qwen 2.5 Instruct
Category
3B
7B
14B
32B
Common Sense
7.0 +0.3 (64.2)
5.4 +0.1 (60.8)
3.9 #0.6 (75.8)
3.7 #0.5 (79.5)
Culture
11.7 1009 (55.8)
16.5 128 (64.8)
14.0 10.1 (76.4)
15.2 10.6 (78.9)
Logic
10.9 10.5 (36.2)
8.4 (52.5)
10.4 (65.3)
40. (74.4)
Knowledge/QA
6.8 +1.3 (47.3)
15.0 =10 (58.4)
23.4 (76.9)
15.3 +1.9 (69.4)
Language
8.7 (.1.00
9.2 40.6 (45.1)
9.5 (59.2)
8.6 (60.2)
Liberal Arts
8.7 =0.7 (65.3)
6.6 +0.7 (74.2)
5.3 4..4 (78.6)
5.3 #0.3 (81.9)
Math
7.7 10.4 (35.4)
4.2 104 (47.3)
6.2 10.7 (57.9)
4.5 11.5 (64.4)
Math (Gen)
2.5 10.4 (8.4)
2.6 #1.0 (12.8)
2.8 10.6 (14.5)
3.9 11.7 (18.7)
Seience/Tech
6.5 +0.3 (45.6)
5,4 +0.4 (56.5)
4.5 10.5 (65.2)
4.4 40.3 (69.7)
Total
8.5 10.1 (50.2)
8.2 10.3 (58.6)
8.0 10.3 (69.7)
6.7 10.5 (72.3)
Table 3: Instruction Tuning: Forgetting (Part 2 of 3)
Qwen 2.5 Coder Instruct
Category
3B
7B
14B
32B
Common Sense
11.7 1100 (59.5)
8.1 +0.3 (67.2)
6.1 +0.2 (70.7)
4.6 +0.4 (77.9)
Culture
15.0 #2.6 (45.7)
19.0 (60.8)
16.6 #1. (66.9)
19.1 #1,1 (73.8)
Logic
17.2 10.6 (41.2)
14.1 10.2 (51.9)
6.9 10.2 (64.0)
5.8 10.3 (69.8)
Knowledge/QA
12.6 +0.1 (48.0)
14.4 (56.4)
14.3 +0.3 +0.3 (64.3)
17.6 +1.0 (77.7)
Language
15.1 +0.7 (36.4)
13.7 =10 (43.2)
10.3 108 (52.7)
8.6 40.5 (59.2)
Liberal Arts
13.9 #0.6 (59.2)
9.. (67.6)
7.8 #0.4 (74.6)
6.7 #0.2 (77.7)
Math
8.9 10.9 (32.8)
6,8 402 (40.8)
6.2 10.4 (54.4)
4.9 10.6 (58.6)
Math (Gen)
2.8 10.3 (7.8)
3.6 11.1 (10.6)
3.0 10.7 (11.8)
2.8 10.4 (13.4)
Science/Tech
9.4 +0.7 (40.5)
8.4 4.5 (52.0)
7.0 +0.2 (59.9)
5.6 +0.5 (65.1)
Total
13.0 +0.2 (48.2)
10.9 10.3 (57.6)
8.9 +0.3 (66.2)
8.4 10.1 (72.2)
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Table 4: Instruction Tuning: Forgetting (Part 3 of
Category
Llama 3.1 Instruct (8B)
Common Sense
6.9 #04 (64.5)
Culture
25.3 +22 (79.1)
Logic
10,9 10. (42.5)
Knowledge/QA
20.6 (60.8)
Language
10.4 #0.7 (39.9)
Liberal Arts
7.6 106 (64.6)
Math
7.3 109 (30.5)
Math (Gen)
1.6 403 (3.7)
Science/Tech
5.9 409 (45.5)
Total
10.8 10.3 (54.5)
Table Instruction Tuning: Backward Transfer (Part of 3)
Qwen 2.5 Instruct
Category
3B
7B
14B
32B
Common Sense
10.8 #0.4 (69.2)
17.5 #0.3 (76.9)
8.7 #0.1 (82.1)
7.5 +0,3 (84.6)
Culture
7.9 124 (49.2)
5.2 119 (45.3)
5.3 10.3 (63.7)
3.0 11.1 (62.1)
Logic
10.2 10.7 (33.6)
14.6 10.5 (60.4)
13.3 10.5 (75.6)
9.0 10.3 (80.4)
Knowledge/QAA
13.3 #2.8 (55.8)
+20 (52.5)
5.1 =10 (S7.9)
7.7 #1.7 (61.3)
Language
#0.5 (29.8)
=0.4 (41.4)
7.2 =0.0 (54.0)
8.3 #24 (57.0)
Liberal Arts
7.2 412 (63.3)
5.7 20.6 (73.0)
5.6 +0.9 (78.9)
4.6 11.1 (80.9)
Math
18.9 #0.9 (51.0)
19.0 208 (67.3)
17.8 +1.4 (73.8)
15.9 +19 (80.0)
Math (Gen)
14.9 10.9 (20.8)
18.7 114 (28.9)
21.6 +10 +1.0 (33.3)
18.6 +1.9 (33.4)
Scienec/Tech
11.4 #0.8 (52.1)
11.9 #0,9 (65.2)
10.4 +1. (73.0)
9.5 =08 (76.5)
Total
10.5 #0.5 (52.7)
11.0 #0.5 (62.1)
8.9 #0.4 (71.3)
8.2 +0.7 (74.3)
Table 6: Instruction Tuning: Backward Transfer (Part 2 of 3)
Qwen 2.5 Coder Instruct
Category
3B
7B
14B
32B
Common Sense
10.9 #0.1 (58.3)
10.6 (70.5)
10.2 =08 (76.2)
6.9 +0.1 (80.9)
Culture
109 (31.0)
4.2 40.7 (37.8)
2.8 40.5 (47.0)
2.7 10.7 (50.0)
Logic
6.6 1.6403022 (25.3)
11.7 20.7 (47.2)
11.5 10. (70.6)
10.8 40.1 (76.4)
Knowledge/QA
9.2
(60.6)
4.8 #1.5 (64.2)
Language
4.7
(29.4)
2.. (48.0)
6.5 +0.7 (55.2)
Liberal Arts
5.8 (48.4)
6..7 (63.3)
41.1
5.1 10.8 (75.6)
Math
14.4 (41.0)
21.7 11.4 (61.2)
17.5 119 (69.6)
19.1 11.5 (77.8)
Math (Gen)
9.3 11.3 (14.3)
14.7 126 (21.7)
20.3 126 (29.1)
24.1 113 (34.7)
Science/Tech
8.7 1.6 (39.5)
10.2 #0.5 (54.3)
10.4 +100 (64.44
9,8 +04 (70.6)
Total
7.4 +0.2 (40.4)
9.3 #0,6 (54.9)
8.6 +0.3 (65.8)
7.6 +0.4 (71.2)
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Table Instruction Tuning: Backward Transfer (Part of 3)
Category
Llama 3.1 Instruct (8B)
Common Sense
11.2 #0.3 (70.3)
Culture
3.9 +02 (46.2)
Logic
17.0 109 (49.6)
Knowledge/QA
10.8 #23 (52.9)
Language
8.9 #14 (36.3)
Liberal Arts
9.8 11.7 (67.5)
Math
19.3 10.7 (46.9)
Math (Gen)
11.8 +0. (14.0)
Science/Tech
15.1 +1.3 (57.8)
Total
11.4 +1.0 (55.1)
1.2 DOMAIN-CONTINUAL PRETRAINING
Numerical results corresponding to Figure covering Qwen 2.5 Coder (3B-32B) and Qwen 2.5
Math (7B).
Table Domain-Continual Pretraining: Forgetting (Part of
Qwen 2.5 Coder
Category
3B
7B
14B
32B
Common Sense
11.9 +0.5 (64.2)
9.0 =0.4 (60.8)
10.4 1066 (75.8)
7.5 +0.6 (79.5)
Culture
11.7 =0.7 (55,8)
10.9 20.4 (64.8)
10.8 +0.7 (76.4)
8.8 +0.8 (78.9)
Logic
5.7 10.2 (36.2)
9.5 20.2 (52.5)
7.6 20.3 (65.3)
8.4 10.1 (74.4)
Knowledge/QAA
5.6 10. (47.3)
6.0 40.8 (58.4)
13.7 (76.9)
3.8 +0.5 (69.4)
Language
10.6 (31.0)
8.4 (45.1)
8.6 (59.2)
7.4 11.3 (60.2)
Liberal Arts
6.4 =0.5 (65.3)
7.0 +0.3 (74.2)
5.1 (78.6)
5.2 +0.3 (81.9)
Math
3.8 #0.9 (35.4)
7..0 (47.3)
6,2 #0.2 (57.9)
7.8 +0.5 (64.4)
Math (Gen)
4.6 10.2 (8.4)
7.1 11.1 (12.8)
8.3 11.0 (14.5)
10.0 123 (18.7)
Science/Tech
4.0 10.5 (45.6)
5.9 10.3 (56.5)
6.4 10.5 (65.2)
5.7 10.5 (69.7)
Total
6.8 100 (50.9)
7.6 10.2 (60.4)
8.0 10.1 (71.8)
6.4 102 (74.6)
Table 9:
Pretraining: Forgetting (Part 2 of 2)
Category
Qwen 2.5 Math (7B)
Common Sense
13.6 =08 (60.8)
Culture
17.8 =03 (64.8)
Logic
9.8 =04 (52.5)
Knowlcdge/QA
9.8 (58.4)
Language
11.3 :09 (45.1)
Liberal Arts
20.0 113 (74.2)
Math
7.5 =1. (47.3)
Math (Gen)
7.0 1.3 (12.8)
Science/Tech
14.4 (56.5)
Total
12.9 204 (60.4)
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Table 10: Domain- Conain-Conntiual Pretraining: Backward Transfer (Part of
Qwen 2.5 Coder
Category
3B
7B
14B
32B
Common Sense
8.3 #0.4 (59.5)
13.8 #0.3 (67.2)
6.6 =0.3 (70.7)
6.3 =0.5 (77.9)
Culture
6.3 12.1 (45.7)
10.0 11.3 (60.8)
5.8 10.4 (66.9)
5.6 -02 (73.8)
Logic
9.4 10.5 (41.2)
9.7 102 (S1.9)
7.7 108 (64.0)
5.3 104 (69.8)
Knowledge/QA
6.6 #1.1 (48.0)
5.4 (56.4)
2.7 =0.3 (64.3)
12,4 (77.7)
Language
8.9 10.9 (36.4)
7.6 408 (43.2)
5.3 2.355.5555 (52.7)
7.8 28=22299.2
Liberal Arts
2.0 10.2 (59.2)
2.2 20.3 (67.6)
2.3 20.3 (74.6)
2.1 =0.1 (77.7)
Math
2.4 +0.2 (32.8)
3,2 +0.3 (40.8)
3,4 =1222226 (54.4)
3,4 (58.6)
Math (Gen)
=0. (7.8)
4.9 #0, (10.6)
5.7 =09 (11.8)
4.7 =0.1 (13.4)
Science/Tech
1.0 #0.3 (40.5)
2.8 #0.0 (52.0)
2.6 =0.1 (59.9)
2.4 =0.2 (65.1)
Total
5.1 #0.2 (48.2)
6.2 #0.1 (57.6)
4.2 =02 (66.2)
5.1 =0.2 (72.2)
Table 11:
Pretraining: Backward Transfer (Part 2 of 2)
Category
Qwen 2.5 Math (7B)
Common Sense
12.7 =05 (59.6)
Culture
6.9 208 (46.3)
Logic
11.9 102 (53.9)
Knowledge/QA
=18 (55.3)
Language
=0.2 (36.3)
Liberal Arts
1.6 =0.3 (49.5)
Math
4.3 =0.5 (43.1)
Math (Gen)
4.5 =0x (10.3)
Science/Tech
3.2 =0.7 (40.7)
Total
6.3 =0.2 (50.1)
1.3 TRAINED FROM BASE
Numerical results corresponding Figure covering SFT/RL reasoning training from base model checkpoints: Qwen 2.5 Math Instruct (7B), QwQ-32B, DeepSeek-R1-Disiil Qwen (7B and 32B), and DeepScck-RI-Disg -D-istiilll Llama (8B).
Table 12: Trained from Base: Forgetting (Part 1 of 2)
Qwen
Distill Qwen
v2.5 Math Inst. (7B)
QwQ (32B)
Category
32B
Common Sense
26.0 (59.6)
3.2 40.4 (79.5)
8.9 +0.3 (59.6)
3.9 10.3 (79.5)
Culture
27.0 =2.4 (46.3)
15.7 10.6 (78.9)
19.3 #2.1 (46.3)
18.8 40.7 (78.9)
Logic
16.8 10.4 (53.9)
2.8 40.2 (74.4)
4.4 10.4 (S3.9)
2.3 10.3 (74.4)
Knowledge/QA
25.4 #2.0 (55.3)
13.2 #2.1 (69.4)
20.1 +0.7 (55.3)
15.3 +1.3 (69.4)
Language
18.2 #0.9 (36.3)
7.9 (60.2)
9.4 20.8 20.8 (36.3)
6,9 +19 (60.2)
Liberal Arts
23.7 10.7 (49.5)
3.1 101. (81.9)
7.6 10.4 (49.5)
3.6 10.3 (81.9)
Math
13.1 +0.3 (43.1)
1.5 403 (64.4)
2.2 20.4 (43.1)
1.8 +05 (64.4)
Math (Gen)
2.1 10.4 (10.3)
1.1 +02 (18.7)
0.8 40.3 (10.3)
1.3 +02 (18.7)
Science/Tech
17.1 40.9 (40.7)
2.2 40.2 (69.7)
4.6 102 (40.7)
2.4 10.3 (69.7)
Total
21.4 +0.5 (50.1)
5.4 403 (72.3)
8.7 20.1 (50.1)
6.0 +02 (72.3)
43

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Published as a conference paper at ICLR 2026
Table 13: Trained from Base: Forgetting (Part 2 of 2)
Category
R1 Distill Llama (8B)
Common Sense
8.1 +02 (64.5)
Culture
29.0 =1.0 (79.1)
Logic
4.8 1:02 (42.5)
Knowledge/QA
11.7 =0.7 (60.8)
Language
10.4 +0.4 (39.9)
Liberal Arts
9.4 10.9 (64.6)
Math
3.3 10.3 (30.5)
Math (Gen)
0.6 #02 (3.7)
Science/Tech
6.4 40.4 (45.5)
Total
9.3 +0.3 (54.5)
Table 14: Trained from Base: Backward Transfer (Part of 2)
Qwen
R1 Distill Qwen
v2.5 Math Inst. (7B)
QwQ (32B)
Category
7B
32B
Common Sense
6.0 106 (32.9)
8.9 10.5 (87.0)
11.4 10.1 (63.0)
8.7 10.3 (85.9)
Culture
3.4 11.3 (9.0)
2.9 110 (59.8)
5.6 (2311)
2.5 106 (55.4)
Logic
8.44 (42.8)
11.8 =0.5 (86.7)
19.7 +0.1 (74.9)
11.7 20.4 (87.2)
Knowledge/QA
(31.2)
9.0 (65.2)
#24
6.8 #1.3 (60.6)
Language
106 (20.9)
9.7 428 (60.6)
8.1 8.1
10,2 +2.7 (62.9)
Liberal Arts
5.0 10.4 (24.5)
6.3 -1. (86.1)
12.7 (56.2)
6.1 (85.2)
Math
15.1 #1.6 (46.5)
22.5 #22 (92.2)
34.3 (85.88
22.1 +25 (91.4)
Math (Gen)
23.6 #3.1 (31.7)
54.8 113 (72.3)
20.422(60.0)
50.3 42.3 (67.7)
Science/Tech
4022 (27.4)
13.8 (85.0)
18.5 (59.3)
13.3 +1.0
Total
6.7 #02 (30.1)
10.3 #0.6 (78.6)
11.0 (57.4)
10.0 #0.6 (77.5)
Table 15: Trained from Base: Backward Transfer (Part of 2)
Category
R1 Distill Llama (8B)
Common Sense
12.6 #0,8 (70.5)
Culture
2.8 104 (36.5)
Logic
28.9 10.1 (74.6)
Knowledge/QA
10.8 (61.8)
Language
12.1 #1.0 (42.2)
Liberal Arts
10.3 +1.5 (65.8)
Math
35.0 40.5 (72.8)
Math (Gen)
49.2 +1. (52.4)
Science/Tech
+1. (62.7)
Total
15.4 #0.6 (62.1)
1.4 TRAINED FROM INSTRUCT HIGH DATA SCENARIO
Numerical results corresponding to Figure 6 covering large-scale SFT/RL reasoning post-training from instruction-tuned models: Open'Thinke (7B), OpenThinker2 (32B), OpenThiikerr3 (7B), Open- CodeReasoner, OpenMath2, and Intellect-2 (32B).
44

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Published as a conference paper at ICLR 2026
Table 16: Trained from Instruct High Data Scenario: Forgetting (Part of
INTELLECT-2
OpenThinker
Category
32B
v1 (7B)
v2 (32B)
v3 (7B)
Common Sense
1.9 #0.3 (87.0)
18.1 #1.3 (76.9)
3,9 #033 (84.6)
9.5 #0.4 (76.9)
Culture
0.7 10.6 (60.2)
13.3 1446 (45.3)
7.6 121 (62.1)
16.4 1:455 (45.3)
Logic
0.6 10.1 (87.4)
7.4 1...2.2. (60.4)
1.0 102 (80.4)
5.2 1.2 (60.4)
Knowledge/QA
2.5 #0.6 (65.9)
13.4 (52.5)
288 #05 (61.3)
5.6 +0.9 (52.5)
Language
1.9 10.3 (62.7)
10.7 11:2 (41.4)
4.2 404 (57.0)
12.7
(41.4)
Liberal Arts
1.2 10.2 (86.1)
15.8 (73.0)
2.3 10.1 (80.9)
9.8 1066 (73.0)
Math
0.9 #0.1 (91.9)
8.3 +0.8 (67.3)
1.0 #0.3 (80.0)
4.1 #0.1 (67.3)
Math (Gen)
4.0 #0.3 (72.3)
4.6 +0.8 (28.9)
1.5 #0.4 (33.4)
1.7 #0.6 (28.9)
Safety/Truth
0.9 #0.2 (66.8)
8.6 +0. (50.0)
3.3 #02 (64.5)
8.7 #0.6 (50.0)
Science/Tech
1.6 10.1 (85.0)
12.2 10.5 (65.2)
2.1 102 (76.5)
6.4 10.3 (65.2)
Total
1.3 #0.1 (79.0)
12.6 +1.5 (62.0)
2.9 #02 (74.3)
8.4 +0.5 (62.1)
Table 17: Trained from Instruct High Data Scenario: Forgetting (Part 2 of 2)
Open Math
Nemotron Code Reasoning
Category
8B
7B
14B
Common Sense
28.5 10.9 (64.5)
22.1 10.1 (76.9)
20.9 124 (79.0)
Culture
49.9 +1.9 (79.1)
21.8 43,1 (45.3)
20.9 10.7 (63.7)
Logic
24.2 +0.5 (42.5)
14.3 40.4 (60.4)
14.7 +0.3 (75.6)
Knowledge/QA
33.8 114 (60.8)
11.9
12.
Language
20.9 (39.9)
21.2
18.4 (62.4)
Liberal Arts
32.1 1:2.3 (64.6)
19.4
12.8 110 (79.6)
Math
17.1 #14 (30.5)
10.6
13.3 #0.4 (73.8)
Math (Gen)
1.9 +0.5 (3.7)
112.91
+1.0 (33.3)
Safety/Truth
19.5 10.7 (36.3)
14.2 (50.0)
12.3 112 (63.0)
Science/Tech
24.8 (45.5)
18.2 (65.22
13.0 10.2 (73.0)
Total
28.8 10. (54.5)
17.7 10.3 (62.4)
14.9 10.0 (72.1)
Table 18: Trained from Instruct High Data Scenario: Backward Transfer (Part of 2)
INTELLECT-2
OpenThinker
Category
32B
v1 (7B)
v2 (32B)
v3 (7B)
Common Sense
10.1 (86.7)
6.1 10.8 (60.9)
4.2 405 (85.0)
10.2 (73.2)
Culture
1.1 10.3 (60.8)
5.7 17.6 (33.6)
2.9 103 (54.3)
4.0 11.0 (23.3)
Logic
0.7 #0.1 (87.4)
13.7 +0.6 (67.4)
6.2 (87.5)
17.2 #09 (76.3)
Knowledge/QA
3.4 #0.9 (67.5)
7.0 (43.0)
3..
20.0 #0.3 (66.6)
Language
1.7 (62.7)
8.8
6.7107
6.8 10. (33.6)
Liberal Arts
=0.1 (86.0)
5.5 (59.2)
(85.44))
(67.5)
Math
0.9 +0.2 (92.1)
11.1 +1.0 (71.3)
14.7 20,3 (81.2)
Math (Gen)
6.1 40.7 (74.3)
18.9 10.5 (43.3)
37.5 122 (69.5)
38.7 11. (66.0)
Safety/Truth
1.3 10.5 (67.2)
7.2 1122 (48.1)
4.1 108 (65.5)
7.5 109 (48.5)
Science/Tech
1.5 10.1 (85.0)
10. (58.7)
7.6 103 (83.8)
9.4 105 (69.2)
Total
1.4 10.1 (79.2)
7.7 +0.5 (55.1)
5.3 102 (77.3)
9.7 402 (62.8)
45

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Published as a conference paper at ICLR 2026
Table 19: Trained from Instruct High Data Scenario: Backward Transfer (Part of 2)
Open Math
Nemotron Code Reasoning
Category
8B
7B
14B
Common Sense
5.7 +0.4 (34.1)
5.2 +0. (54.3)
3.6 #1.2 (56.0)
Culture
0.5 10.4 (6.5)
5.1 12. (19.2)
5.2 10.5 (37.8)
Logic
6.0 10.4 (18.3)
13.7 10.7 (57.4)
8.1 10.3 (65.4)
Knowledge/QA
2.8 +1. (25.3)
5.0 (42.6)
3.2 (46.6)
Language
7.2 108 (18.1)
4.9 40.8 (23.3)
7.6 10.88 (46.3)
Liheral Arts
2.9 10.2 (25.5)
4.3 (52.8)
4.2 10.5 (68.3)
Math
6,9 #1.1 (15.0)
10.4 +0.0 (56.4)
10.7 #0.8 (69.4)
Math (Gen)
14.6 #14 (16.5)
15.8 +21 (33.44
22.0 +16 (47.6)
Safety/Truth
4.0 #0.6 (15.9)
8.6 (42.5)
6.8 10.6 (55.6)
Science/Tech
3.6 10.9 (17.0)
5.2 100 (47.7)
6.4 10.1 (64.3)
Total
4.2 +0.2 (21.1)
6.7 +0.4 (46.5)
5.8 +0.1 (59.0)
1.5 TRAINED FROM INSTRUCT Low DATA SCENARIO
Numerical results corresponding to Figure 20 covering light-touch reasoning post-training from instruction-tuned models: $1.1 (7B, 14B, 32B) and LIMO (vl and v2).
Table 20: Trained from Instruct Low Data Scenario: Forgetting (Part of 2)
s1.1
Category
7B
14B
32B
Common Sense
8.1 10.4 (76.9)
5.1 +1.0 (82.1)
4.7 +:05 (84.6)
Culture
12.1 #1.2 (45.3)
10.6 +0.4 (63.7)
7.5 +0. (62.1)
Logic
7.2 1:0.1 (60.4)
4.2 104 1044 (75.6)
2.8 102 (80.4)
Knowledge/QA
5.7 1:11 (52.5)
2.3 13.1
11.2 (61.3)
Language
8.6 +0.8 (41.4)
6.7
4.. (57.0)
Liberal Arts
7.4 +0.5 (73.0)
5.4 (78.9)
+0.1 (80.9)
Math
6.6 1.088 (67.3)
5.8 109 (73.88
3.2 104 (80.0)
Math (Gen)
5.7 #0.4 (28.9)
2.8 +0. (33.3)
1.4 +0.1 (33.4)
Safety/Truth
7.5 40.9 (50.0)
5.3 (59.4)
4.5 10.4 (64.5)
Science/Tech
7.2 10. (65.2)
4.8 106 (73.0)
3.3 404 (76.5)
Total
7.7 402 (62.1)
5.3 402 (71.3)
3.9 10.1 (74.3)
Table 21: Trained from Instruct Low Data Scenario: Forgetting (Part of 2)
LIMO
Category
v1 (32B)
v2 (32B)
Common Sense
3.5 #0.3 (84.6)
3.0 =0.3 (84.6)
Culture
3.7 11.9 (62.1)
4.4 10.9 (62.1)
Logic
3.0 10.3 (80.4)
6.1 10.4 (80.4)
Knowledge/QA
2.0 =0.4 (61.3)
2.4 203 (61.3)
Language
3.8 (57.0)
4.2 40. (57.0)
Liberal Arts
+0.0 (80.9)
2.3 20.3 (80.9)
Math
1.3 10.1 (80.0)
1.7 =0.1 (80.0)
Math (Gen)
1.1 +0.5 (32.9)
1.2 =0.3 (32.4)
Safety/Truth
2.9 +0.3 (64.5)
2.8 =0.2 (64.5)
Science/Tech
2.3 10.1 (76.5)
1.9 102 (76.5)
Total
2.6 #0.1 (74.3)
3.0 =0.2 (74.3)
46

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Published as a conference paper at ICLR 2026
Table 22: Trained from Instruct Low Data Scenario: Backward Transfer (Part 1 of
s1.1
Category
7B
14B
32B
Common Sense
7.3 +0.5 (73.8)
4.6 +0.3 (81.5)
4.5 #:02 (84.3)
Culture
5.7 1:0.5 (35.0)
4.6 10. (54.2)
4.6 1:04 (56.7)
Logic
13.8 10666 (68.8)
9.2 10.55811.) (81.1)
5.7 10.4 (83.9)
Knowledge/QA
13.1 +0.3 (59.3)
11.9 +13 (69.0)
11.3 +0.5 (71.2)
Language
10.2 10.77 (44.7)
7.2 (55.7)
6.6 10.5 (60.4)
Liheral Arts
6.3 6.37 (71.5)
5.3 1.3.6.600 (78.9)
5.0 +0.1 (82.4)
Math
11.5 +0.3 (74.2)
12.0 40.7 (81.9)
9.4 +0.9 (87.8)
Math (Gen)
14.3 +0.7 (37.6)
20.5 40.3 (51.0)
32.2 +4.3 (64.3)
Safety/Truth
9,4 #0.1 (52.5)
7.4 +09 (62.2)
5.2 40.4 (65,4)
Science/Tech
8.5 10.1 (66.8)
7.9 10. (77.3)
7.6 10.5 (82.2)
Total
9.1 #0.2 (63.5)
7.2 +0.3 (73.5)
6.2 +0.2 (76.9)
Table 23: Trained from Instruct Low Data Scenario: Backward Transfer (Part 2 of 2)
LIMO
Category
vI (32B)
v2 (32B)
Common Sense
3.9 10.4 (85.1)
4.2 10.2 (86.3)
Culture
7.5 40.8 (66.1)
6.7 2088 (64.6)
Logic
5.8 +0.3 (84.6)
5.3 #0.6 (79.6)
Knowledge/QA
11.1 113 (71.2)
8.0 +0.7 (67.7)
Language
5.6 40.7 (60.3)
5.3 #0.3 (59.3)
Liberal Arts
4.7 10.2 (83.7)
4.9 10.2 (84.4)
Math
9.6 (90.7)
10.1 +08 (90.8)
Math (Gen)
32,6 #4.9 (64.5)
35.1 +2, (66.4)
Safety/Truth
5.0 10.4 (67.1)
4.5 10.7 (66.7)
Science/Tech
7.2 10.3 (83.2)
7.5 10.2 (84.0)
Total
6.2 102 (78.8)
5.8 10.3 (77.9)
1.6 QWEN2.5 BASE AND CODER MERGE (RELATIVE TO QWEN2.5 BASE)
Numerical results for the high-drift coder merge (see Figures 25 and 26). measuring forgetting and backward transfer relative to the Qwen2.5 Base checkpoint. Both linear and Slerp interpolation produce substantial forgetting across all categories, reflecting the 87% weight-drift ratio that makes interpolation destructive.
Table 24: Qwen2.5 Base and Coder Merge: Forgetting
Linear (a)
Slerp (a)
Category
0.2
0.8
0.2
0.8
Common Sense
8.2 10.6 (67.2)
11.3 10.7 (67.2)
13.9 1:2 (67.2)
16.2 16.4 (67.2)
Culture
12.0 +2. (60.8)
15.8 118 (60.8)
21.1 1:34 (60.8)
41.3 43.3 (60.8)
Logic
11.6 #0.4 (51.9)
19.4 +1.0 (51.9)
23.4 (51.9)
17.7 +0.9 (51.9)
Knowledge/QA
4.6 10.4 (56.4)
8.1 10.3 (56.4)
10.9 11.0 (S6.4)
16.2 11. (56.4)
Language
10.4 (43.2)
11.2 10.2 (43.2)
14.0 10.5 (43.2)
12.7 104 (43.2)
Liberal Arts
2.5 40. (67.6)
4.1 +0.9 (67.6)
5.8 #1.6 (67.6)
8.9 +0.5 (67.6)
Math
4.5 +1.2 (44.9)
8.9 +2.1 (44.9)
41.9 (44.9)
9,4 +0.7 (44.9)
Safety/Truth
1.9 10. (49.9)
3.7 406 (49.9)
6.0 10.3 (49.9)
4.7 11.0 (60.4)
Science/Tech
2.6 10.6 (52.0)
5.2 40.7 (52.0)
6.7 #1.3 (52.0)
4.4 409 (52.0)
Total
6.1 402 (58.0)
9.3 40.4 (58.0)
12.3 402 (58.0)
13.9 +0.4 (59.0)
47

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Published as a conference paper at ICLR 2026
Table 25: Qwen2.5 Base and Coder Merge: Backward Transfer
Linear (a)
Slerp (a)
Category
0.2
0.8
0.2
0.8
Common Sense
8.3 =0.4 ()
9.8 #0,5 (65.2)
9.2 =0.2 (61.0)
7.2 #0,9 (55.3)
Culture
4.1 108 (46.8)
6.2 118 (46.1)
8.3 12.0 (44.0)
2.6 10.5 (15.1)
Logic
5.3 10.6 (43.7)
4.2 10.1 (31.0)
3.7 20.1 (24.6)
4.4 102 (33.3)
Knowledge/QA
8.3 =0.4 (61.6)
9.2 =0.4 (58.2)
(53.4)
5.1 #0.6 (44.1)
Language
4.2 (39.1)
7.2 (37.4)
6.2
4.6 10.5 (30.7)
Liberal Arts
20.4 (67.3)
6.1 21.6 (70.2)
2.7
2.1 20.3 (58.7)
Math
5.5 (45.0)
6.5 #1.6 (39.9)
=2 (34.0)
4.6 1144 (36.3)
Safety/Truth
2.4 =07 (51.8)
5,0 #1, (52.7)
6.8 (51.5
3.0 1100 (57.9)
Science/Tech
2.7 =0.3 (51.4)
5.6 11. (52.3)
5.2 =16 (49.7)
2.3 #0.3 (48.1)
Total
4.8 =0.1 (56.0)
6,5 #0.7 (53.9)
6.4 =0, (49.9)
4.0 #0.1 (46.1)
1.7 QWEN2.5 BASE AND CODER MERGE (RELATIVE TO QWEN2.5 CODER)
Companion tables to I.6, measuring the same Qwen2.5 Base Coder merge relative the Qwen2.5 Coder checkpoint. The large forgetting figures confirm that the merged model fails to preserve Coder capabilities as well, underscoring the bidirectional damage caused by high weight drift.
Table 26: Qwen2.5 Base and Coder Merge: Forgetting
Linear (a)
Slerp (a)
Category
0.2
0.8
0.2
0.8
Common Sense
10.2 #0.7 (70.5)
12.7 20x8 (70.5)
15.2 #2.5 (70.5)
18.2 +7.0 (70.5)
Culture
6,8 +199 (37.8)
10.6 114 (37.8)
15.8 22332233774 (37.8)
28.5 +3.2 (37.8)
Logic
15.1 #0.9 (47.2)
20.4 =0.7 (47.2)
=0.5 20.
18.8 +0.3 (47.2)
Knowledge/QA
6.5 108 (52.5)
8.0 10.6 (52.5)
9.8 10.3
14.0
Language
10.6 (29.4)
40.5
9.1 (29.4)
9.2 #09 (29.4)
Liberal Arts
6.6 =0.1 (63.3)
6.1
7..7 (63.3)
12.3 +0.3 (63.3)
Math
17.1 10.5 (58.9)
19.6 112 (58.9)
22.2 12.1 (58.9)
20.8 10.3 (58.9)
Safety/Truth
10.9 11.7 (42.0)
10.0 114 (42.0)
10.6 (42.0)
11.2 112 (43.4)
Science/Tech
10.3 #0.4 (54.3)
10.3 #09 (54.3)
11.6 +19 11. (54.3)
11.9 +0.3 (54.3)
Total
9.5 #03 (54.0)
11.1 #0.4 (54.0)
13.4 103 +0.3 (54.0)
15.3 20.9 (54.1)
Table 27: Qwen2.5 Base and Coder Merge: Backward Transfer
Linear (a)
Slerp (a)
Category
0.2
0.8
0.2
0.8
Common Sense
7.9 10.4 (67.4)
8.7 10.5 (65.2)
8.0 113 (61.0)
6.8 11.6 (55.3)
Culture
13.4 11.7 (46.8)
15.8 132 (46.1)
17.8 (44.0)
6.2 (15.1)
Logic
11.4 10.2 (43.7)
+03.3.3.00 (31.0)
6.0 (24.6)
7.8 +000 (33.3)
Knowledge/QA
15.6 11.0 (61.6)
14.3 -0.2 (58.2)
12.2 21.2 (53.4)
8.7 +1.5 (44.1)
Language
10.0 (39.1)
12.2 =0.3 (37.4)
9.9 (32.1)
9.1 +0. (30.7)
Liberal Arts
9.6 =9.4 (67.3)
11.2 =0.9 (70.2)
10.8 10.3 (67.6))
8.5 +0.2 (58.7)
Math
7.7 11.0 (45.0)
7.0 20,9 (39.9)
5.1
5.2 108 (36.3)
Safety/Truth
8.7 (39.1)
10.3 -1.3 (42.4)
9.1 (39.9)
9,2 (41.0)
Science/Tech
8.1 102 (51.4)
10.6 (52.3)
106 (49.7)
7.2 102 (48.1)
Total
10.0 -0.3 (54.8)
10.2 (52.9)
9.3 +07 (48.8)
7.5 +0.1 (44.4)
I.8 QWEN2.5 INSTRUCT AND OPENTHINKER 7B MERGE (RELATIVE TO QWEN2.5 INSTRUCT)
Numerical results for the moderate-drift OpenThinker merge (see Figures 27 and 28 measuring forgetting and backward transfer relative Qwen2.5 Instruct. The low weight-driff ratio (1.8%)
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Published as a conference paper at ICLR 2026
translates into comparatively modest forgetting, and the Linear (0.8) interpolation yields near-zero forgetting across most categories.
Table 28: Qwen2.5 Instruct and OpenThinker 7B Merge: Forgetting
Linear (a)
Category
0.2
0.8
Logic
8.7 1.5 (75.2)
3.6 10.4 (75.2)
Knowledge/QA
6.5 =0.4 (60.3)
4.2 #2. (60.3)
Liberal Arts
9.4 10.8 (73.0)
3.7 10.1 (73.0)
Math
10.5 10.6 (65.3)
3.3 10.5 (65.3)
Safety/Truth
7.7 =13 (55.1)
2.8 #0.4 (55.1)
Science/Tech
9.6 =0.3 (67.4)
4.2 #0.2 (67.4)
Total
8.7 =0.2 (66.1)
3.6 #0.5 (66.1)
Table 29: Qwen2.5 Instruct and Thinkeinker 7B Merge: Backward Transfer
Linear (a)
Category
0.2
0.8
Logic
6.0 =0.7 (71.6)
4.6 (76.6)
Knowledge/QA
5.2 #18 (58.6)
3.8 =099 (59.8)
Liberal Arts
4.6 10.5 (66.6)
3.9 10.6 (73.3)
Math
8.8 =0.3 (63.1)
6.8 =0.5 (70.1)
Safety/Truth
5.8 =0.3 (52.7)
3.4 =0.6 (56.1)
Science/Tech
6.3 (62.9)
4.9 20.3 (68.1)
Total
6.1 #0.1 (62.6)
4.6 (67.3)
1.9 QWEN2.5 INSTRUCT AND OPENTHINKER 7B MERGE (RELATIVE TO OPENTHINKER)
Companion tables to 1.8, measuring the same Qwen2.5 Instruct- Oaae--ppnTTiiii Thinker merge relative to the OpenThinker checkpoint. The backward transfer values here reflect the degree which general instruction-following capabilities are transferred into the merged model as seen from the reasoning model's perspective.
Table 30: Qwen2.5 Instruct and Thennhinker 7B Merge: Forgetting
Linear (a)
Category
0.2
0.8
Logic
7.1 =08 (64.0)
3.7 #1.7 (64.0)
Knowledge/QA
7.3 11.6 (52.2)
7.0 11.2 (52.2)
Liberal Arts
8.5 (59.2)
5.5 10.4 (59.2)
Math
14.3 =1.6
9.4 #1.5 (72.1)
Safety/Truth
10.3 22.3 (52.2)
7.2 41.3 (52.2)
Science/Tech
9.2 =0.2 (60.2)
6.8 #0,6 (60.2)
Total
9.4 =0.6 (60.0)
6.6 10.6 (60.0)
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Published as a conference paper at ICLR 2026
Table 31: Qwen2.5 Instruct and OpenThinker 7B Merge: Backward Transfer
Linear (o)
Category
0.2
0.8
Logic
12.8 11.6 (71.6)
13.1 #3.7 (76.6)
Knowledge/QA
12.1 12.5 (58.6)
12.6 120 (59.8)
Liberal Arts
14.1 10.2 (66.6)
16.1 10.6 (73.3)
Math
7.5 11. (63.1)
7.9 #1.1 (70.1)
Safety/Truth
10.7 (52.7)
10.1 (56.1)
Science/Tech
11.2 #0.8 (62.9)
12.7 404 (68.1)
Total
11.4 20.7 (62.6)
12.1 11.0 (67.3)
1.10 QWEN2.5 INSTRUCT AND OPENTIINKER3 7B MERGE (RELATIVE QWEN2.5 INSTRUCT)
Numerical results for the high-drift OpenThinker3 merge (see Figures 23 and 24). measuring for- getting and backward transfer relative to Qwen2.5 Instruct. The 20% weight-drift ratio drives catastrophic forgetting: values in the 30-40% range across categories reflect the output collapse described in the qualitative analysis in F..
Table 32: Qwen2.5 Instruct and OpenThinker3 7B Merge: Forgetting
Linear (a)
Category
0.2
0.8
Logic
33.6 1.27 (75.2)
118 (74.2)
Knowledge/QA
25.7 18.0 (60.3)
18.0 15.2 (45.0)
Liberal Arts
35.0 #2.9 (74.4)
28.8 +1.7 (73.8)
Math
40.1 #6.1 (65.3)
30.0 =0.7 (65.3)
Safety/Truth
36.2
(64.1)
22.8 1337 (61.0)
Science/Tech
#3.2 (67.4)
29.6 +009 (67.4)
Total
33.5 23.7 (67.8)
26.0 10. (63.8)
Table 33: Qwen2.5 Instruct and Open 7B Merge: Backward Transfer
Linear (a)
Category
0.2
0.8
Logic
1.3 120 (31.3)
2.3 =100 (39.9) .)
Knowledge/QA
1.9 11.0 (28.8)
1.2 114 (21.7)
Liberal Arts
1.9 =0.8 (30.2)
2.9 =0.1 (39.2)
Math
0.7 (12.6)
1.6 10.8 (27.0)
Safety/Truth
11.1 (15.8)
2.9 10.6 (34.6)
Science/Tech
2.5 =1. (30.4)
2.5 =02 (30.9)
Total
#1.1 (24.9)
2.2 =0.3 (31.7)
I.11 QWEN2.5 INSTRUCT AND OPENTHINKERS 7B MERGE (RELATIVE TO OPENTIINKERR3
Companion tables to I.10, measuring the same Qwen2.5 Instruct-Opent Pettuct-OpenThhnnker merge relative to Thenkhin checkpoint. The extreme forgetting values (including Math values above 50%) confirm that the merged model fails retain the reasoning capabilities of OpenThinker3 regardless of the interpolation coefficient.
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Published as a conference paper at ICLR 2026
Table 34: Qwen2.5 Instruct and OpenThinker3 7B Merge: Forgetting
Linear (o)
Category
0.2
0.8
Logic
28.1 #3.2 (65 4)
25.1 #2.6 (66.9)
Knowledge/QA
21.2 16.4 (54.3)
17.2 17.3 (38.6)
Liberal Arts
31.3 12. (68.9)
27.2 1177 (68.3)
Math
52.3 #6,9 (81.1)
41.8 118 (81.1)
Safety/Truth
33.5
(59.8)
23.5 (58.6)
Science/Tech
32.0 23.5 (70.7)
32.2 114 (70.5)
Total
33.1 238 (66.7)
28.0 1.2 (63.8)
Table 35: Qwen2.5 Instruct and OpenThinkee 7B Merge: Backward Transfer
Linear (a)
Category
0.2
0.8
Logic
3.0 =5.0 (31.3)
4.9 =09 (39.9)
Knowledge/QA
2.2 10.7 (28.8)
4.5 123 (21.7)
Liberal Arts
2.3 =0.8 (30.2)
5,4 =07 (39.2)
Math
(12.6)
1.5 =0.2 (27.0)
Safety/Truth
10.8 (15.8)
5.5 :0.4 (34.6)
Science/Tech
2.0 20.7 (30.4)
2.8 =0.3 (30.9)
Total
11. (24.9)
4.0 20.4 (31.7)
DISCLAIMER FOR USE OF LLMS
We primarily used LLMs in coding co-pilot applications to facilitate experimentation and help with plotting code for result presentation. LLMs were also used as writing tools to assist in refining the paper. However, the final version was carefully reviewed and finalized by the authors. No LLMs were used in idcation and experimental design.
51


Reviews:
[{"review_id": "s1ExZreK3r", "reviewer": "Reviewer_f1yS", "summary": "This study introduces a sample-wise framework to quantify how LLMs forget or retain knowledge during post-training. By measuring 1→0 (forgetting) and 0→1 (backward transfer) transitions, it finds that instruction tuning and reasoning post-training improve capabilities with minimal forgetting, while domain-continual pretraining causes moderate forgetting.", "questions": "N/A", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper introduces a sample-wise, chance-adjusted metric to precisely quantify forgetting and backward transfer, overcoming the limitations of aggregate accuracy measures.\n- This paper conducts a broad empirical studies across multiple model sizes, training regimes (SFT, RLHF, instruction tuning, continual pretraining), and datasets, providing a systematic view of post-training dynamics.\n- This paper offers interest findings: instruction tuning and reasoning post-training yield strong backward transfer with little forgetting, while domain-continual pretraining induces moderate loss.\n- This work challenges the assumption of “catastrophic forgetting” in post-trained LLMs, showing that most modern post-training does not erode pretrained knowledge as severely as previously thought.", "weaknesses": "- This study focuses mainly on MCQ datasets, which may not generalize to open-ended tasks or generative reasoning.\n- The uniform random-guessing correction assumes independence and equal likelihood of options, which oversimplifies model behavior and may distort real forgetting dynamics.\n- While correlations between data scale, post-training type, and forgetting are documented, the paper does not deeply analyze why certain stages (e.g., reasoning training) cause particular effects.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EtPHl2UKE5", "reviewer": "Reviewer_6fZS", "summary": "This paper focuses on the critical problem of pretrained knowledge forgetting and backward transfer in large-scale language model (LM) post-training, addressing the limitations of traditional evaluation methods. Unlike traditional task-averaged metrics that obscure individual knowledge changes, the paper defines forgetting as 1→0 transitions (correct before post-training, incorrect after) and backward transfer as 0→1 transitions (incorrect before, correct after), recognizing that knowledge samples (e.g., facts about U.S. presidents, API syntax) are non-fungible. To eliminate the interference of random guessing in multiple-choice benchmarks, the paper proposes \\(\\boldsymbol{F_{true}}\\) (true forgetting) and \\(\\boldsymbol{BT_{true}}\\) (true backward transfer). These metrics subtract the expected \"chance transitions\" (e.g., lucky guesses turning to errors) from raw transitions, with additional ceiling metrics (\\(F_{max}\\), \\(BT_{max}\\)) to contextualize the theoretical upper limit of knowledge change.", "questions": "Please see Weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1-The sample-wise paradigm directly addresses the non-fungibility of pretrained knowledge (a long-overlooked limitation of task-averaged metrics), and chance-adjusted metrics rigorously correct for random guessing—an essential step for reliable evaluation of multiple-choice benchmarks (the dominant format for knowledge-intensive LM tests).\n\n2-The methodology is both theoretically sound (e.g., explicit assumptions about uniform guessing and independent pre/post events) and practically feasible (no need for logits or repeated sampling, enabling large-scale deployment).\n\n3-The findings provide clear practical guidance: e.g., \"instruction tuning is low-risk for knowledge retention\" and \"large-scale RL/SFT on instruction-tuned models requires better controls\"—directly informing how practitioners design post-training pipelines.\n\n4-The work reconciles conflicting views between continual learning literature (predicting catastrophic forgetting) and LM practice (observing minimal forgetting in many regimes), highlighting the need to adapt continual learning theories to LM-specific post-training.", "weaknesses": "1-The paper identifies what post-training regimes cause forgetting/transfer but rarely explains why. For example:\nIt notes \"culture\" is the most forgettable category across regimes (e.g., Llama-3.1-8B-Instruct has 18.9% forgetting in culture), but does not investigate whether this stems from cultural knowledge being less \"entrenched\" in pretraining, or post-training data mismatching cultural contexts. It finds reasoning training on base models outperforms instruction tuning in transfer, but does not clarify if this is due to the \"scratchpad\" mechanism, data quality, or objective function—limiting the paper’s ability to guide how to design better post-training objectives.\n\n2-The paper states that for RL/SFT on instruction-tuned models \"at larger scales, effects are mixed and warrant further study with better controls\" (1-5, 1-116), but provides no actionable path to resolve this ambiguity. It does not test variables like data diversity (e.g., mixed vs. narrow domains), training duration, or objective function (RL vs. SFT)—leaving a critical gap in understanding a common post-training scenario.\n\n3-The paper concludes \"model merging does not reliably mitigate forgetting\" but only tests two-checkpoint merging (pre vs. post-training). This contrasts with prior work (e.g., Yadav et al., 2023) that uses 8+ checkpoints to mitigate weight drift. The failure to explore multi-checkpoint merging or alternative methods (e.g., TIES-merging) weakens the conclusion, as it does not rule out merging as a viable strategy—only the specific implementation tested.\n\n4-All evaluations use MCQ formats, but real-world LM use cases often involve generative tasks (e.g., open-ended QA, text synthesis). The paper does not validate whether its sample-wise framework extends to generative settings, limiting its generalizability to practical applications where \"forgetting\" might manifest as ungrammaticality or factual errors in free text.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UGinrmlX2C", "reviewer": "Reviewer_wF4s", "summary": "This paper addresses the core issue of knowledge change during LLM post-training, proposing an innovative and practical analytical framework. The authors first correctly identify two major shortcomings of existing average accuracy-based evaluation methods: the inability to decouple forgetting and backward transfer, and susceptibility to random guessing in multiple-choice question evaluation.\n\nTo overcome these problems, the paper proposes a sample-level metric paradigm that provides a more granular analytical perspective by tracking the transitions of individual samples from correct to incorrect and from incorrect to correct before and after training. A chance-adjusted method is introduced to model random guessing behavior, extracting the chance factor from observed transitions to estimate F_true and BT_true, which more closely approximate the actual knowledge change.\n\nBuilding on this framework, the authors conducted the largest empirical study to date, systematically evaluating knowledge change across different model families and sizes in various mainstream post-training scenarios, including domain-based continuous pre-training, instruction fine-tuning, inference training, and model merging. The study yielded several important findings.", "questions": "see weakness", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Shifting the evaluation perspective from the macro-level average accuracy of the task to the micro-level of the sample is a highly insightful transformation. This allows us to understand more precisely the dynamic changes in the knowledge within the model, rather than simply seeing a vague final score. \n\n2. The authors evaluated nearly 30 model-training combinations, covering the vast majority of mainstream post-training paths in the current LLM ecosystem. This work itself is a massive undertaking, providing the community with extremely valuable data and baselines. The \"forgetting map\" constructed in the paper provides practitioners and researchers with a practical \"benchmark\" to measure the \"knowledge cost\" of different post-training strategies. This has direct practical value in guiding future designs for more efficient and less forgetting training processes.", "weaknesses": "1. The model's assumption of \"uniform random guessing when unable to solve a problem, with independent guesses before and after\" is somewhat idealistic. Discussions could be made regarding robustness checks using multiple sampling/deterministic decoding, or question-level chance correction based on logits, to relax the independence and uniformity assumption.\n\n2. There are some shortcomings in the experiments. The current version reports a flipped variance/confidence interval, requiring significance and multiple comparison correction. Using random decoding with a temperature of 0.6 and a nullus value of 0.95, and running only once without multiple seed repetitions, introduces randomness leading to flipping. The variance and stability of the flipped statistics are not evaluated. The current evaluation primarily covers MCQs and has not yet been extended to tasks closer to the post-training objectives, such as open generation, code/tool ​​calls, and procedural derivation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "f9LCaoGv3w", "reviewer": "Reviewer_e6NM", "summary": "This paper introduces a sample-wise framework to measure knowledge forgetting and backward transfer in language models during post-training, using chance-adjusted metrics to account for guessing in multiple-choice evaluations. Through large-scale experiments across various post-training stages, the study finds that instruction tuning and reasoning training from base models cause minimal forgetting but significant backward transfer, while domain-continual pretraining leads to moderate forgetting with limited improvement. The authors also show that model merging does not reliably mitigate forgetting, challenging common assumptions in continual learning and providing a practical tool for tracking knowledge retention in evolving AI systems.", "questions": "See weaknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "1. The experiment is extensive\n2. The research topic is realistic", "weaknesses": "1. Limited Conceptual Contribution and Analytical Depth\nThe paper reads more as an extensive experimental report than a work offering novel theoretical insights. The findings—such as \"moderate forgetting\" or \"lower forgetting\"—are described using vague, qualitative terms and do not reveal unexpected patterns or advance conceptual understanding of forgetting mechanisms in language models.\n\n2. Methodological Rigor in Experimental Design\nThe evaluation framework does not adequately control for key training variables, such as learning rates or data volume across stages. For instance, observing less forgetting in SFT and RL compared to pretraining is unsurprising given the typically smaller learning rates used in later stages. Similarly, greater backward transfer in reasoning training may simply reflect the base model's initial lack of exposure to such data, rather than meaningful generalization.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)