PolySkill: Learning Generalizable Skills Through Polymorphic Abstraction For Continual Learning
Authors: Simon Yu
Gang Li
Weiyan Shi
Peng Qi
Venue: ICLR 2026 Poster (2026)
Keywords: Skill Induction
Agent
Polymorphism
Continual Learning
Large Language Models
Primary area: foundation or frontier models, including LLMs
TL;DR: We proposed PolySkill, a framework that guides Web Agents to induce skills that is generalized and transfer better across different websites, unlike existing methods that produce over-specialized, non-transferable skills.

Abstract: Large language models (LLMs) are moving beyond static uses and are now powering agents that learn during their interaction with external environments. For example, agents can learn reusable skills while navigating web pages or toggling new tools. However, existing methods for skill learning often create skills that are over-specialized to a single website and fail to generalize.
We introduce PolySkill, a new framework that enables agents to learn generalizable and compositional skills. The core idea, inspired by polymorphism in software engineering, is to decouple a skill's abstract goal (*what* it accomplishes) and its concrete implementation (*how* it is executed). Experiments show that
our method (1) improves skill reuse by 1.7x on seen websites and (2) boosts success rates by up to 9.4\% on Mind2Web and 13.9\% on unseen websites, while reducing steps by over 20\%. (3) In self-exploration settings without specified tasks, our framework improves the quality of proposed tasks and enables agents to learn generalizable skills that work across different sites. 
By enabling the agent to identify and refine its own goals, the PolySkill enhance the agent a better curriculum, leading to the acquisition of more generalizable skills compared to baseline methods. Our findings show that separating a skill's goal from its execution is a crucial step toward developing autonomous agents that can learn and generalize across the open web continuously. Our code can be found in \href{https://github.com/simonucl/PolySkill}{\texttt{https://github.com/simonucl/PolySkill}}.

Published as a conference paper at ICLR 2026
POLYSKILL: LEARNING GENERALIZABLE SKILLS THROUGH POLYMORPHIC ABSTRACTION FOR CON-
TINUAL LEARNING
Simon Yu² Gang Li
Weiyan Shi
Peng Qi?²
1 Northeastern University Co-Supervision
Uniphore
[yu. chi, we we.shh//eoortneateenn.co edu, {gang. -i, peng. peng. eiJeunipoore.com
ABSTRACT
Large language models (LLMs) are moving beyond static uses and are now pow- ering agents that learn during their interaction with external environments. For example, agents can learn reusable skills while navigating web pages or tog- gling new tools. However, existing methods for skill learning often create skills that are oper-scccillized to single website and fail to generalize. We introduce PolySkill, a new framework that enables agents to learn generalizable and com- positional skills. The core idea, inspired by polymorphism in software engineer- ing, is to decouple a skill's abstract goal (whar it accomplishes) and its concrete implementation (how executed). Experiments show that our method (1) im- proves skill reuse by 1.7x on seen websites and (2) boosts success rates by up 9.4% on Mind2Web and 13.9% on unseen websites, while reducing steps by over 20%. (3) In self-exploration settings without specified tasks, our framework improves the quality of proposed tasks and enables agents to learn generalizable skills that work across different sites. By enabling the agent to identify and re- fine its own goals, the PolySkill enhance the agent better currieulum, leading to the acquisition of more generalizable skills compared to baseline methods. This work provides a practical path toward building agents capable of continual learn- ing in adaptive environments. Our findings show that separating skill's goal from its execution is a crucial step toward developing autonomous agents that can learn and generalize across the open web continuously. Our code can be found in https: / / thub. com/ momooonll/eee /Pol 15kill
INTRODUCTION
LLMss have enabled significant progress in agent development (Yao al., 2023b; Yang al., 2024; Agashe et al., 2025). Web agents represent key class of LLM-based agents, where the agents navigates complex Graphical User Interfaces (GUIs) to achieve user-defined goals (Deng et al., 2023; Zhou et al., 2024a; Cheng et al., 2024; Wu et al., 2024b; Chen et al., 2025b; Gou et al., 2025; Xue et al., 2025). However, primary challenge lies in developing generalizable agents that can operate robustly across different tasks within single website, as well as transfer skills to distinct websites within the same functional domain (e.g., generalizing across different airline booking interfaces). To be generalizable, these agents must learn from their experiences, allowing them to adapt when faced with new tasks or unseen websites (Silver & Sutton, 2025; Shen et al., 2025).
One promising direction is skill induction: learning reusable skills from past experiences. This ap- proach was first explored in an open-ended environment by Voyager (Wang et al., 2023). Agent Workflow Memory (Wang et al., 2024) pioneered skill induction for web agents, which used natural language skills to prove the concept's viability. Consequently, Agent Skill Induction (Wang al., 2025) and SkillWeaver (Zheng et al., 2025) made these skills more robust by structuring them as code. However, these methods primarily focus on same webxite, cross-task settings. By optimizing

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Published as a conference paper at ICLR 2026
Polymorphism Skill for Web Agents
amazon
TARGET
search
search
search
farc
add
checkiout
checkout
V checkout
(I) Improved Skill Usage and Utility
(2) Prevent Catastrophic Forgetting
Contioual Learaing)
Baseline
StillWore
ASI
PolySkill (Ours)
Shapping
1.3%
1.7x
1.6x
Web Shopping
+2.2%
+4.9%
1.8x
PolySkill (Ours)
Taak Sacces Raiz
skill Reauliiity
Talk Coverage
Skil Compasiiouilty
Skill Induction Heration
Figure 1: PolySkill, a novel approach that enables web agents to develop polymorphic skills that generalize across websites. PolySkill achieves superior performance with 1.3-1.8x improvements in task success rate, skill reusability, and compositional capabilities compared to existing methods.
for performance on familiar websites, they generate over-specialized skills that fail to generalize, leaving the critical challenge of cross-website generalization under-explored. This challenge high- lights fundamental tension between a skill's specificity and its generalizability. This leads us key research questions. First, how can induce skills that are transferable across diverse websites? Second, beyond task success, how can we quantitatively measure skill transfer and reuse? Answering these questions is crucial step toward the broader vision of agent autonomy, where an agent could discover such generalizable skills on its own through open-ended exploration (Liu et al., 2025a).
To address these questions, we introduce PolySkill, framework that grounds agent skill learning in the principle of polymorphic abstraction, cornerstone of object-oriented design from software en- gineering (Milner, 1978). This paradigm separates the abstraction from its actual implementations. We apply this to skills by (1) defining an abstract class (e.g., 'Abst ract wwwaacthhoppnngg hwpppinnnittt ") common interface for domain, specifying high-level goals like the method
that serves "search(query, filter)". Concrete subclasses (c.g., 'AmazonSite vide the distinct, website-specific implementations. This allows the agent to operate stract level and create compositional skills that are not tied to any specific website's functional- ity, decoupling them from brittle UI changes across websites. This approach also enhances bet- ter composition, allowing the agent to chain together abstract operations in the parent class like search-proocee () addToCart (), and checkout () to execute complex, multi-step tasks; while preventing the need to implement the compositional skills per website.
'Target ') then pro-
ab-
To validate our approach, we address a critical gap existing evaluation methods. Prior work on skill induction primarily relies on final task success rates (Zhou et al., 2024a). However, this metric reveals only part of the picture: confirms that an agent succeeded, but not how. It cannot distin- guish between success from efficiently reusing learned skills and success from solving task from scratch, which makes difficult to measure the value of the skill induction. To provide a clearer picture and answer our second research question, we introduce additional metrics, including Skill Reusability and Task Coverage, to directly diagnose the skill transfer failures that plague existing methods. Our evaluation reveals clear improvement: while prior methods show Skill Reusability below 18% on unseen websites. skills learned via PolySkill achieve a 31% reuse rate. Further- more, present the first comprehensive evaluation of skill induction recent open-source agentic
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Published as a conference paper at ICLR 2026
models, such as Qwen3-Coder (Qwen, 2025) and GLM-4.5 (GLM, 2025), demonstrating that our findings are robust and not limited to proprietary models.
Finally, extend our analysis to a task-free, continual learning setting (Zheng et al., 2025; Liu et al., 2025a), which tests an agent's ability to explore multiple websites and induce skills with- out predefined tasks. Our results suggest that this is viable path toward self-improving agents, as the PolySkill better guides exploration than previous unstructured approaches (Zheng et al., 2025). More broadly, we believe the principle of polymorphic abstraction extends beyond the web, offer- ing a promising direction for developing transferable skills for any agent that operates in diverse environments with shared structural patterns (Xu al., 2025). By grounding skill creation in poly- morphism, this work takes an important step toward continual learning, enabling agents to build a library of adaptive skills that can evolve with experience.
RELATED WORK
Memory and Skill Acquisition for Agentic Learning An agent's ability to generalize is fundamen- tally tied to how it represents skills, Current approaches often store skills in concrete formats, such as natural language descriptions in prompt libraries (Wang et al., 2024; Zhu al., 2025) brittle action traces from successful task executions (Wang et al., 2025). While more robust, programmatic representations, where skills are stored executable code, also face limitations. These learned pro- grams are typically concrete implementations tailored to a specific context (e.g., one website's UT), hindering their reuse across varied environments that serve similar function (Wang et al., 2023; Zheng et al., 2025). These methods lack mechanism for abstracting the semantic intent of skill away from its specific implementation, which is crucial for flexible adaptation.
We address this gap with a novel, hybrid skill representation inspired by object-oriented design. While broader work has focused on the architecture of agent memory, such as using episodic streams (Park et al., 2023) or managed external stores (Chhikara et al., 2025; Fang et al., 2025), our contri- bution lies in the representation of the skill itself. We define a skill with an abstract interface that captures its semantic purpose, which can be linked to multiple, interchangeable concrete implemen- tations. Drawing on established findings that abstraction and structure enhance agent reasoning and robustness (Wu et al., 2024a; Yao et al., 2023a), this polymorphic structure provides the formal ben- efits of programmatic skills while explicitly incorporating the flexibility required for generalization across different web pages.
Continual Learning A long-standing goal in Al is continual learning (van de Ven et al., 2025; Liu et al., 2025a), where an agent continually learn during testing time and its interaction with the environments. This is specifically true for web agents, where interaction is more effective than just thinking longer (Shen et al., 2025; Liu al., 2025b). key insight from AI research is that this requires compositional generalization, the ability to learn primitive concepts and recombine them to solve novel problems (Jiang al., 2025). Existing web agents that learn from experience, such as those using exploration enhanced by curriculum learning (Zheng et al., 2025) or reinforcement learning (Zhou et al., 2024b), perform a simple form of continual learning by adding new skills to a library. However, their skills lack a compositional structure; learned code snippet or action trace cannot be easily modified or combined with others in a principled way, leading to low utilization rates on websites. Our work, first showing the effectiveness of the principles for modularity from software engineering, offers a powerful solution. Polymorphism (Milner, 1978) is a time-tested concept designed explicitly manage variation between implementations while maintaining stable interface. By applying this principle to agent skills, our approach provides a structured paradigm for learning capabilities that are modular and interchangeable. See Appendix B for detailed related work.
3 POLYSKILL: POLYMORPHISM-GUIII AGENT SKILL INDUCTION
Our approach addresses the fundamental challenge of learning agent skills that balance specializa- tion with generalization. We propose a hierarchical framework that separates skill learning into three complementary stages: skill discovery through polymorphic abstraction, skill refinement through compositional verification, and skill deployment through adaptive execution.
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3.1 PRELIMINARY
Problem Formulation. We model the web agent's interaction environment as a Partially Observable Markov Decision Process (POMDP), defined by the tuple Ap, T, S2, O) Here, S is the latent state space representing the full underlying state of the web application. Ap is the set of primitive actions the agent can execute on a webpage (e.g., cl iick (e (element), , type (text) ), as defined in Appendix E.2. The function T S Ap + (SS) is the stochastic state transition function. Since the agent cannot perceive the entire state S, receives an observation On E Ω (c.g.. the Ally tree and viewport screenshot) each timestep & through the observation function O S A(2).
LM-based Agent Policy. We consider an agent driven by a large language model (LM) backbone, L. The agent's 's policy, Tc. determines the next action based on its current context. This context consists of a working memory M, which stores the high-level task instruction and the history of observations and actions, and dynamic skill library Kr. The skill library contains reusable skills that expand the agent's 's full action space to A, Ap U Kr, Each skill & E Ke is parameterized sequence of actions k(args) := a1 + action E Ar, which includes both primitive actions (App) and learnt skills (K2). The policy is thus denoted as <aaa ot, Mr, K.),, which we shorten Tc
an where denotes sequential execution and each
Task Execution and Objective. The agent's goal is to complete a task specified by natural lan- guage instruction q. each timestep t, the agent receives an observation or, updates its memory Mr. and selects an action a, E Ar using its policy. This interaction over horizon H generates a trajectory T = (0o. do, 01, (1,...., Ow1- , A task considered successful the trajectory satisfies goal condition, indicated by success function g(T,9) 1. Our central objective is to induce an effective skill library IC. We formalize this by maximizing an efficiency-awaad aware reward, max K. & (g(TT))) ->| where the penalty on trajectory length |r] incentivizes the creation of compact and reusable skills. While this objective could be optimized as a loss function, we instead this efficiency principle to guide our agent's 's prompting.
The core challenge, which PolySkill addresses, is to populate K. with skills that are both effective for specific contexts (specialized) and transferable to new tasks/domains (polymorphic).
3.2 THE POLYSKILL FRAMEWORK
Limitation of existing skill induc- tion methods We identified the limitations of current skill induction methods. We tested two state-of of the-
Success Rate (%)
Skill Unlizztion Rate (%) SkillWeaver
ASI
15.0
art approaches, ASI and Seivereaver, on how well their learned skills trans- fer to unseen websites. Their ex- ample of skills can be seen in Ta- ble 4. As illustrated in Figure 2, our analysis revealed two key problems. First, the learning process can be unstable, producing over-specialized skills. For instance, SkillWeaver's performance with Claude-3.7-Somme degrades over time because its self- proposed tasks become increasingly complex and specific. This causes the resulting skills to be too intri- cate and poorly suited for general- ization. Second, these skills show poor generalization when applied to new websites. This is reflected in ex- tremely low Skill Reusabilitys seen websites: less than 9% for ASI and less than 3% for SeivrWeaver.
Success Rate (%))
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of lleeations
Figure 2: Limitations of existing skill induction meth- ods. We evaluate ASI and SkillWeaver across two foun- dation models: S GPT-4.1 and * Claude-3.7-Soma Both methods show unstable learning dynamics and poor skill reusability, demonstrating over-specialization issues that hurt performance on WebArena Shopping tasks.
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Published as a conference paper at ICLR 2026
PolySkill Abstract Class
PolySkill Implementation
The Abstract besain Class, defining "schem" for shopp ing sites, class abstractShoga (Shogaingsttee ing
class AnszonWehs dbstrrattshhoptsttttii
amazon
product) self, citest search har. 14)
def search_profuct(sel. query: str]:
FL11 www.aaaarrbbbaa... 1d, every)
***Sorrches ches for product. **.
"tatur")
def add, to, cartisetr, iten, 1d: str, quantity: int): Add spectfied iten to the shopp cart, MM
def add, ta. carttsetr, itea, tdi str, quaniity trt): clecsiate betton,
def (heckout(salf):
def chackost (awif);
-NITE: Zmp iaplaments checiost a corpastte actionn: first
IInitiatte the checkout process."""
self.viev carti)
Compos Itional Skills
def find and_ to cart, query: str, itea_ str):
class pppptteesstsataatteettttere
search, sel, query: str):
O TARGET.
*** ches for product and adds it to the cort. self wtt.seeeepr_dcccttc product(
ftSl(search bar. query)
self to___ddd______aaaae_eea toditt__crrttttont
def wid. to. carl(aelf, Ltwe_ld: str, quantity: let 1):
def purchase, item(self, query: str, itee. id: stri:
clicklads button, (d)
***pinds spectricc product, adds it to the cart, and starts
FLll[quattiyy bar 1d, quancity) el clickladd_ wtw.:ttttcrr.bbtttooco.
checkout, .**
self. Ftd__and__dd to_cart(querr, (toa_id)
self. checkaut()
def checkouttsetr))
cLick]shackout, button_id] id]
Table 1: Example of PolySkill. (Left) shows the high-level abstraction of the skills under shopping domains; (Right) shows the website -pesite implementation across shopping domains, built upon the Abstract Shopping parent class. Note that the compositional skills would not need be rede- fined, since it solely rely on the compositionality of other skills.
We build on prior work that demonstrates the robustness of representing skills as code (Wang et al., 2025; Liu et al., 2025a). To specifically address the brittleness and over-specialization, we intro- duce a solution inspired from software engineering: polymorphism. This allows our framework to separate skill's abstract goal from its concrete, site-specific implementation.
However, these skills are often tied to one specific website's ' design, making them hard to reuse on other sites. Other methods, like SkillWeaver (Zheng et al., 2025), create robust skills by generating complex code from the agent's s experience. As shown in Figure 2, these skills use very specific code to find elements brittle: they work well on the original website but break easily on new sites with different layouts.
page, limiting them only one site. This over-specialization makes the skills
Our Proposed Solution We introduce PolySkill, framework that solves this problem by learning a dorain-driven skill hierarchy. Instead of treating skills as isolated scripts, we organize them into classes based on a website's 's category. For example, skills for Amazon and Target are treated as concrete implementations of an abstract Abstree ract ShoppingSit class. This structure allows the agent to learn a general "schema" for type of website and then fill in the specific, reliable implementations for each new site it encounters.
3.3 SKILL INDUCTION PROCESS
Base of Skill Induction Pipeline Our skill induction process is built upon the robust verification pipeline established by ASI (Wang et al., 2025). In their framework, skill creation begins after a task successfully completed using a sequence of primitive actions. An LLMM-based induction mod- ule analyzes this successful trajectory to propose one or more programmatic skills that encapsulate reusable parts of the workflow (Pan al., 2024). Before the skills are added to the library, a verifi- cation phase is done where the agent attempts to solve the same task again, this time by executing the newly generated skill. Only if this new execution is deemed as successful the skill considered validated and added to the agent's library for future use.
Innovation via Polymorphic Skill Induction Where our method, PolySkill, improves is by inte- grating this process with a polymorphic skill structure. A critical preliminary step in our framework is that if the agent is operating on its first shopping website, it must first induce the high-level ab- stract class, Abst ra www..thhppp......c..oc across shopping-relater skills. Subsequently, during the skill induction phase, as the agent induces new skills on a specific site (e.g., amazon com), it is guided to first register the corresponding function signature within the abstract class, and then define the concrete implementation within the site-specific class (e.g., AmazonWebsite, which inherits the Abstract ingppiinnnntteee This reframes the induction prompt: rather than asking the LLM to directly induce skills, we instruct it to create general abstract skill first and then implement the specific search method for an
te, which provides common ground of skills signature
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Algorithm PolySkill: Polymorphic-Drine Driven Skill Induction
Input: A sequence of tasks {q......9N J. LM Policy ne. LM Judge Vc
2: Initialize: Dynamic skill library Ko +
3: for t= l......,
do
4: Let q: be the current task from Q.
5: Define the agent's ' full action space: A. t Ap K
6:
T Execute Eas 9t, Ac)
1. Execute task to generate trajectory
7:
if Ve(T, 9t) = then
D 2. Verify trajectory correctness
8:
Knew InduceSkill( (,,, K(--1)
3. Induce new hierarchical skills
9:
K + K U Knew
> Update skill library for the next task
else
11:
K4 + Ko-1
Skill library remains unchanged on failure
end if
13:
end for
14:
return KNN
AmazonWebsite class. This encourages the agent to learn skills that are not just locally effective but are structurally consistent implementations of shared, domain-wide concept.
Skill Learning on Unseen Websites This polymorphic structure makes learning on new websites within known category significantly more efficient. Imagine the agent has already formed the AbstracctShpppingg..
class derived from its initial interaction with amazon com, and now visits walmart, com for the first time. It immediately recognizes Walmart as a shopping site and retrieves the abstract blueprint. This blueprint provides the agent with a clear set of exploration goals. Instead of randomly trying actions, it knows it needs to figure out how to concretely im- plement abstract skills like search. rearch.product agent successfully searches for item, it follows the standard induction process create new Walmartwebst i e class, filling in the search.produree product method with the specific actions that worked. This guided approach accelerates learning by focusing the agent's s efforts mastering the essential skills defined the abstract interface. The pseudocode is shown in Algorithm 1.
and addLto_ca oocart on this new site. Once the
3.4 EVALUATION SETUP
We evaluate our induction process over baseline, in two different settings: (1) Task-Defined Bench- marks standard benchmark settings, we apply this process within controlled environments, in- cluding Mind2Web (Deng et al., 2023) and WebArena (Zhou et al., 2024a). Here, the agent is presented with a predefined curriculum of tasks. Each successful trajectory provides the validated sequence of actions needed to implement concrete skill method. This controlled approach al- lows us to rigorously measure how well the polymorphic representation facilitates generalization and transfer to unseen tasks, websites, or domains. (2) Task-Free Continual Learning: To assess the ultimate goal of agent autonomy, we also apply our framework in a task-free setting, similar to settings Voyager (Wang et al., 2023) and SkillWeaver (Zheng et al., 2025). In this scenario, the agent explores websites on its own, proposes its own goals, and induces skills from its successful at- tempts. Crucially, we showed that our polymorphic hierarchy enables structured exploration (Murty et al., 2025; Gandhi & Neubig, 2025). The already-leamed abstract domain classes act as schema, providing strong prior for what skills are worth discovering (Liang al., 2025).
3.5 EVALUATION METRICS
We assess performance using five key metrics. Two are standard benchmarks adopted from prior work (Wang et al., 2024; 2025), while we introduce three new metrics to measure skills' utility. (1) Task Success Rate (SR) is the percentage held-out tasks the agent completes successfully. This is the primary measure of overall performance. (2) Number of Steps is the average number of actions the agent takes to complete a task, where each primitive action and each call to a skill is counted single step. Fewer steps indicate higher efficiency. (3) Skill Reusability measures the number skill reused in new tasks. A high rate indicates the agent learns relevant, broadly applicable skills rather than overly niche ones. (4) Task Coverage measures the tasks that used least one

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Published as a conference paper at ICLR 2026
Baseline
PolySkill
(-Update)
PolySkill (+Update)
GPT441
Claude-3.7Soonee
68
72
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69
Accuracy
60
66
63
52
60
57
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Cruss- Website
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Owen3-Coder-480B...
Z
GLM-4.5
52
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44
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Cross-tosk
Cross-Website
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Cross-Website
Cross-Domain
Figure 3: Performance comparison of PolySkill with baseline methods the Mind2Web bench- mark across four large language models. The y-axis shows task success rate (%). The three evalua- tion settings the x-axis, Cross-task, Cross- Website, and Cross- represent increasing lev- els of generalization difficulty. Our method, PolySkill, consistently outperforms the ASI baseline, while the online continual update version, PolySkill (+ (+Upae))), achieves state-of-the-ard perfor- mance across all models and settings. The performance gains are most significant in the challenging Cross-Domain scenario, highlighting our method's s superior generalization. Error bars represent the standard error over three runs.
skill. This metric indicates whether the skills can be adaptive in actual test case scenarios. (5) Skill Compositionality measures how frequently the system reuses existing skills building blocks for more complex tasks. A high score indicates an efficient and scalable learning process, as the system leverages its acquired knowledge rather than learning every new skill from scratch. Formal definitions for all metrics are provided in Appendix E.I.
4 EXPERIMENTS
In this section, we present comprehensive evaluation of PolySkill. We first assess its performance and generalization capabilities standard, task defined benchmark settings. We then test its ability learn autonomously in more challenging task-free, exploratory scenario.
4.1 STANDARD BENCHMARK EVALUATION
Benchmarks (1) Mind2Web (Deng et al., 2023) To evaluate PolySkill general web navigation scenarios We evaluate on Mind2Web. a comprehensive dataset spanning 137 websites across 31 do- mains. The dataset includes 2,350 tasks with annotated cross-task, cross-website. and cross-domain settings, providing diverse scenarios for skill learning and evaluation. We use the standard train-test split, holding out entire website categories for out-of-domain evaluation. Since the benchmark only comes with human trajectory data, we employs an automatic judge based GPT-4. for measuring task success rate, which achieved an 85% agreement with human judgment (Xue et al., 2025). We employ the judge both in the skill induction stage and during the test time. (2) WebArena (Zhou et al., 2024a) It provides a realistic evaluation environment with fully functional websites across e-commerce, forums, development tools, and content management systems. The benchmark in- cludes 812 tasks ranging from simple navigation to complex multi-step procedures, with automatic evaluation through functional correctness checks.

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Published as a conference paper at ICLR 2026
Models and Baselines For experiments, evaluate the performance on four foundation mod- els: two closed-souree (GPT-4.1 and Souude-3.7-Soneel and two open-source agentic models (wwen3-Coder-40B-AA.
(Qwen, 2025) and GLM-4.5 (GLM, 2025)). This selection encom- passes leading proprietary models and open-source models that post-training specifically on agentic tasks, enabling rigorous evaluation of agentic capabilities. We compare our method against three baselines; standard agent with no skill induction (Base) and two leading skill induction frame- works, AS/ (Wang et al., 2025) and Skil/Weaver (Zheng et al., 2025). The ASI is induced online and applied to both Mind2Web and WebArena; however, for SkillWeaver, we only evaluate it on WebArena Subsets.
Results. For Mind2Web, as shown in Figure 3 (Results for WebArena are presented in Table 8). Our PolySkill outperforms ASI across both static and online settings. On GPT-4.1, PolySkill im- proves Cross-task accuracy from 52-53% (ASI) to 55-56%, and its online variant jumps to about 63%, compared to only 59% for ASI (+Update). In the hardest Cross-Domain setting. Poly (+Update) reaches 63-64% versus 62% for ASI (+Update). On Qwen3-Coder, PolySkill (+Up- date) improves Cross-task from 41.5% (ASI) to 47.5% and Cross-Domain from 35.2% to 39.9% These consistent gains, particularly the significant jumps in Cross- Website, underscore PolySkill stronger generalization and the clear benefit of continual online updating over ASI.
4.2 ANALYSIS
To investigate how skills are induced during training, we use our newly proposed metrics to track the model's learning dynamics. This analysis builds on previous work that sought to understand these dynamics (Shah et al., 2025). For our study, we saved 20 model snapshots at regular intervals while training on the Mind2Web Cross-task and WebArena Shopping benchmarks. We then evaluated our metrics on each snapshot to observe how skills emerge over time.
Relation between Skill Reusability and No. Steps To validate the fundamental hypoth- esis that skill learning improves task effi- ciency, we analyze the relationship between Skill Reusability and the number of steps re- quired for task completion. The results, pre-
WebArena Shopping: Steps vs Skill Utilization
ASI
Skillweaver
PolySkill
Steps
Utilization (%)
50
Average Number of Stops
50
sented in Figure 4, demonstrate a clear inverse correlation across all three methods: as skill reusability increases from 0% to over 20%, the average number of steps decreases substan- tially from approximately 6.1 to 3,3-4.4 steps. PolySkill achieves the highest Skill Reusabil- ity (reaching 20.4% by task 180), while main- taining competitive step reduction. Notably, ASI shows the most dramatic step reduction de- spite lower utilization rates, suggesting efficient skill application. This analysis confirms that learned skills directly translate to improved task efficiency, with higher utilization rates consis- tently leading to more streamlined task execu- tion. The strong correlation validates our core premise that skill induction and reuse are key drivers of agent performance improvement in complex web navigation tasks.
10
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160
180
Troks Camplettd
Figure 4: Relationship between skill reusability and task efficiency in WebArena shopping tasks. Lines show average steps (left y-axis) while bars show Skill Reusability (right y-axis) for ASI (or- ange), SkillWeaver (green), and PolySkill (red). Higher skill reusability correlates with fewer re- quired steps, demonstrating improved task effi- ciency through learned skills.
Number of Skills Learned We additional analyze the size of the induced skill library ("# Skill"). Ideally, an agent should identify a concise set of "polymorphic" skills that can be reused across various contexts, rather than memorizing a vast library of specific, non-transferable sub-routines. A lower skill count coupled with high accuracy indicates that the agent has successfully distilled the underlying logic of the tasks in certain layouts. We measure the number of skills by measuring how many unique skills are set aside, without considering the exact implementation per class. We include both the w/o Update and w/ Update settings.
As shown in Table 2. PolySkill demonstrates superior efficiency compared to ASI. In
the static setting, PolySkill achieves higher accuracy (55.4% vs. 52.3% in Cross- task) while maintaining smaller skill library (43 50 skills). This confirms
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Training Setting
Evaluation Benchmark (SR % Skill Usage %)
Method
Iterations
WA Shopping
AM7.
Target
Buseline
37.4
1. Soinle-Domain Specialists
WA
50
42.3
14.9
50.2
3.3
61.2
2.8
Target
50
38.0
2.1
48.5
3.5
77.0
52.1
2. Seguential Curviculum
+
75 75
AMZ Target WA
50 50 + 50
38.2
11.9
65.2
43.3
77.3
24.3
Target + AMZ WA
50
39.5
66.
69.2
18.9
WA Target AMZ
50 40+50+55
42.1
10.8
70.5
43.2
76.8
23.3
SkillWeaver
150
39.8
8.6
64.4
25.2
74.2
18.3
3. Self-guided Exploration
AMZ + Target + WA
150
43.1
14.6
66.7
36.4
75.2
19.4
Table 3: Performance in the task-free exploration setting for the Shopping Domain. For the Skill-
Weaver, we selected the best-performing curriculum in (WA- - Target -> AMZ).
our hypothesis that polymorphic skills are more robust to interface variations, allow- ing single skill to cover scenarios that would require multiple distinet skills in ASI.
When online updates are enabled, the skill library naturally grows to accommodate novel environments; however, PolySkill + Update continues outperform the base- line significantly. Notably, in the Cross- task setting, PolySkill Update reaches state-of-ther oo--th--attttttt performance (63. (6) with only 47 skills, far fewer than the 66 skills required by ASI Update. This sug- gests that PolySkill adapts by refining ex- isting polymorphic definitions rather than blindly accumulating redundant skills.
Method
Cross-tade Cross-Webbid Skill
Cross-Domsi
Baseline
53.8
56.2
62.3
Storle Approackes ASI PolySkill
$2.3
54.9
57.3
57.6
60.1
w/ Ouline Upuinte
ASI Updiic
39.4
66
58.7
62.1
PolySkill Updatte
Table 2: Performance comparison on Mind2Web using GPT-4.1. We report success rate (Ace f) and # Skill 4.
Case Study: Continual Learning To simulate how skill would be used and evolved in real-world scenarios, we investigate the agent's continual learning capabilities. The experiment begins with an agent whose skill library is initialized the WebArena Shopping tasks. The agent then continues to perform online update via skill induction on new websites for Amazon and then Target, respectively (details in Appendix E.4). We performed the experiments times reduce the potential variance across run. this setting, we are both interested in the positive transfer from the existing skill library in similar domain. Also, we measure the WA Shopping performance after has adapted to new website. This allows us to study potential catastrophic forgetting, where learning new knowledge can harm existing skills, a well-known challenge in continual learning (van de Ven al., 2025).
The results, presented in Figure 5, highlight two critical advantages of PolySkill's polymorphic ab- straction: First, when adapting new domains like Amazon and Target, PolySkill effectively learns the required specialized skills, demonstrating strong positive transfer (orange and red curves). More importantly, avoids the interference that reduces the ASI performance at the last. After specializing on the new sites, the ASI agent's 's performance on the original WebArena tasks degrades significantly. In contrast, PolySkill retains its general knowledge, with its performance on the original tasks re- maining stable (blue curve). This results in final +4.9% performance advantage over ASI on the benchmark, proving our method's 's ability to learn new skills without hurting existing ones.
4.3 FROM SPECIALIST TO EXPLORER: SKILL LEARNING IN EXPLORATIVE SETTING
To assess the goal of agent autonomy, we test our framework in an explorative, continual learning scenario. This setting extends beyond predefined tasks; instead, the agent explores websites inde- pendently, proposes its own goals, and acquires skills from successful attempts (Zheng et al., 2025).

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We designed an experiment to answer central question: Does agent need human- gunde curriculum to lear general skills, or can its own exploration lead to versatile generalization? To investigate this, compared three learning paradigms using GPT-4.1 actoss shopping sites (AM2, Target, WA) and developer platforms (Gi thub, Gi LTab.. First, we established two baselines to ground our comparison. (1) Single-Domain Specialists, the agent is trained on only one website, allowing us to measure the effects of over-specialization. (2) Sequential Curriculum, the agent follows fixed, predefined order of websites, representing standard pre-defined curriculum. And our proposed method, (3) Self-guided Exploration. This approach extends the self proposig agent concept from SkillWeaver (Zheng et al., 2025) to a more challenging multi-webssin setting where the agent can freely choose which website to explore at each iteration. This autonomy is enabled by our core contribution, the polymorphic skill structure, which provides the necessary framework for the agent to autonomously structure, hone, and generalize its skills across these diverse platforms.
Shopping Domains (OneStopShop, Amazon and Target) As shown in Table 3, the choice of learning paradigm affects skill transfer. Single-domain specialists perform well their home site (e.g., 77.0% SR on Target) but fail to transfer this knowledge, with Skill Reusability below 4% on other sites. The sequential curriculum improves transfer but is sensitive to the order of the curricu- lum. Critically, the fully autonomous agent achieves the highest general success rate (43.1 1%) on the held-out WA Shopping benchmark. One major finding from the results, showing WA OneStopShop actually transfer better to other websites, meaning it has much richer skills to be learned.
Coding Platforms (Gitlab, Github) We further test this hypothesis in more challenging scenario, transferring skills between developer platforms. As detailed in Table 9, the self-guided agent once again demonstrates superior generalization. It achieves the highest success rate on the held-out GitLab benchmark (66.2%) while also attaining the best performance on GitHub (84.0%), proving its ability master both domains concurrently.
This key finding across both experiments demonstrates that PolySkill's hierarchical abstraction en- ables an agent autonomously build robust and general skill set that outperforms methods relying on handcrafted curriculum. It successfully learns to be generalist explorer and skill refiner.
5 CONCLUSION
In this work, introduced PolySkill, framework that teaches web agents generalizable skills using the principle of polymorphic abstraction. By separating a skill's high-level intent from its website-specific implementation, our method enables agents reuse capabilities across diverse vironments. Our experiments show a major improvement in generalization. with 73% of learned skills transferring to unseen websites, a improving improvement to the <31% % achieved by prior methods. This approach resolves a key tension between the need for specialized skills and the adaptability required for the open web, and we confirmed its effectiveness on open-source agentic models, demonstrating its broad applicability.
Looking ahead, this framework opens several avenues for research, including handling highly dy- namic websites, enabling skill sharing between agents, and incorporating human feedback. More broadly, the principle of polymorphism not limited to web agents. It offers a powerful template for any agent that must operate in diverse environments sharing similar underlying structures (Xu et al., 2025) (r22))from from robotics, where skills must generalize across physical settings (Cheng et al., 2025; Liu al., 2025a), to tool use, where software interfaces constantly evolve (Fei et al., 2025; Qiu et al., 2025). This work, therefore, provides a concrete step toward more robust and adaptive agents capable of learning from experience (Silver & Sutton, 2025) in diverse environments.
5.1 FUTURE WORK
To address the challenge of domains where task category boundaries are "fuzzy", a prime candidate for future work is moving from manual definition to autonomous skill clustering. Instead of relying on predefined labels, agents could propose abstract classes based on functional similarity, i.e. via soft-clustering of execution traces, allowing abstract interfaces to emerge organically. Furthermore, we envision enabling smaller models to acquire these polymorphic skills autonomously through RL. In this paradigm, the efficiency penalty (7) would evolve from a fixed heuristic into mechanism for reward shaping. allowing the agent to dynamically learn the optimal trade-off between exploration effort and skill reusability.
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6 ACKNOWLEDGEMENT
We are grateful to our colleagues at Orby (now part of Uniphore) for their insightful discussions, comments, and support in setting up the evaluation environments during Simon's 's internship. We also thank fellow Orby interns Yijin Ni and Mengzhao Jia for their valuable discussions and feedback. Additionally, we appreciate the constructive feedback from the members of the CHATS lab on an early version of this manuscript. We thank Modal Lab and Thinking Machines Lab for their generous sponsorship of computing resources.
REPRODUCIBILITY STATEMENT
The experiments on open-source models are run on an 8-H100 GPU cluster, and all calls for propri- etary LLMs are made via their official API. All experiments are done via BrowserGym (de Chezelles et al., 2025) API, and experiment details are illustrated in Section 4 and Appendix E.
ETHICS STATEMENT
Real World Impacts Smarter autonomous agents could be big help in the real world. They have the potential to make computers much easier to use, especially for people with disabilities or those who lack technical skills. Agents could also automate many routine computer tasks, freeing people up for more creative work. While the agents in our paper aren't 't advanced enough for this yet, these future possibilities mean we need think carefully about the social and economic impact on jobs.
Our own work here focuses on improving performance on research tests, so we don't believe creates any immediate real-world harm. One clear concern, however, is that web agents might misuse websites or violate their terms of use and copyright. We take this seriously and will remove an agent's ability access any site if requested by its owner.
Bias and Safety It's also very important make sure these agents are fair and don't 't harm exclude anyone. Before any agent is deployed, it needs to be checked carefully for hidden biases. Because agents take actions in the world, they could cause serious problems than simple chatbot if proper safeguards aren't 't in place. More research is needed to understand and prevent these potential harms.
Intended Use The methods and models in this paper intended for research purposes only. We use academic benchmarks like WEBARENA and MIND2WER to measure progress in the field. The systems we've built research prototypes and might not yet ready for real-world deployment, especially in high-stakes situations where errors could be costly.
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Skill Transfer Across Websites
WebArena Shopping
ASI
Amazon
Target
PolySkils
Basaline
WebArens Perfomance Score (%)
39
(rom scratch)
+.7%
68
Amazon/Targee Performance Score (%)
36
84
8a
gage
+2.2%
+4.9%
Ba geet
30
-56
+3.6%
-52
24
48
200
300
# Tasks Completed
Figure 5: A continual learning experiment showing PolySkill can prevent catastrophic forgetting. The experiment consists of two phases: initial skill library induced on the in-domain WebArena Shopping benchmark: followed by continual learning cross-webiste: Amazon then Target. The orange and red lines (right y-axis) show that PolySkill learns the new websites more effectively than the ASI baseline. The blue lines (left y-axis) track performance the original WA results throughout the experiment. Shaded regions represent the standard error across three runs.
This appendix provides comprehensive details supporting our main findings. Appendix A docu- ments the use of large language models in our analysis. Appendix C discusses potential limitations of our work. Appendix provides the detailed experiment details, including the definition of eval- uation metrics, action space, and used dataset. Appendix F provides completed benchmark results across datasets and metrics.
A LARGE LANGUAGE MODEL USAGE
We used large language models (LLMs) only for language refinement tasks, including grammar checking, phrasing adjustments, and enhancing readability. All scientific ideas, experiments, analy- ses, and results are the sole contributions of the authors. We only used LLMs for literature searching. It is important to note that the LLM was not involved in the ideation, research methodology, or ex- perimental design. All research concepts, ideas, and analyses were developed and conducted by the authors.
B EXTENDED RELATED WORKS
Adaptive Web Agents The central challenge for web agents is generalizing from training environ- ments the vast, unseen web. (Cheng al., 2024; Zheng et al., 2024; Gou et al., 2025; Gu al., 2025; Chen et al., 2025b). Although the Agashe et al. (2025) seems to have already solved most of the GUT agent tasks, the recent work Xue et (2025) still shows improvement needed to continue to improve agents in complex tasks and robustness. Similarly, Contextual Experience Replay dis- cretizes trajectories into "cxperiences" (containing environment dynamics and skills) and retrieves relevant blocks to guide episodes (Liu al., 2025b).
Skill Representation Formats The representation of a "skill" central design choice in skill- learning agents. The simplest forms are low-level and lack abstraction. Skills Textual Descrip- tions leverage LLMs to store procedural knowledge in natural language within a prompt library (Wang et al., 2024; Zhu al., 2025). This offers flexibility but lacks the formal structure needed for
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ASI Skill Example
Sevver Skill Example
pwrrddooddccctsorrrrr box str, every: str):
asyne def retrieve_ catere_cctterressppgeet
headstoones
search search_areduse produces 555' 'sony bluetsoth
Exomples
Argsi
page A Playrright "Page" instance that controls browser autoratton.
Raturns:
Filli Fillisearch,.ee cuary)
list of diet
teybzard. press! "Enter")
1ist of diettonartes where each dicttonary contatrs the following
gate categoryddrspson str, categary
""aase" [str]: The name of the category.
-"url" [str]: The URL of the category.
Ravigate gate product category rirst clicktng drapdawn mEru.
category. aleeents mestt page.query selector. alll' (tabpaaapare senu senuttwe") categories
element category.
cttckrdrapdiwn.c (d)
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awatt lsvat itnk_element, pet_property(""reee ("href")) )). (t.eee"")__._o__iliio v Viakeelarent **
categortes, append(( "name"; "urt"s categoriee Les
Table 4: Example of ASI Skills and SkillWeaver Skills.
reliable composition or verification. Similarly, Skills as Action Traces store successful action se- quences or workflows from prior tasks (Wang et al., 2024; 2025). While easy to record, these traces implicitly encode the specifics single website's UI, making them fail when even minor elements change. A more structured approach represents Skills as Programs. This was pioneered in inter- active environments like Minecraft, where agents build library of reusable code snippets (Wang et al., 2023), and has been applied to web agents (Zheng et al., 2025) and software engineering (Chen et al., 2025a). However, these learned programs are typically concrete implementations tied to single context, not inherently designed to handle variations across different websites fulfilling the same function.
Architectures for Agentic Memory Beyond the representation of individual skills, recent work has explored the broader architecture of agent memory to support long-horizon reasoning and learning. One line of work focuses processing and structuring episodic experiences. For instance, Gen- erative Agents (Park et al., 2023) structure memory as stream of observations and use LLMs to distill high-level summaries guide behavior. Other research focuses on memory management for efficiency and complex tasks. HiAgent organizes working memory hierarchically by subgoals, sum- marizing and replacing low-level traces improve performance on long- horizon tasks (Hu al., 2024). External-memon
systems such as Mem0 (Chhikara et al., 2025) and MemP (Fang et al., 2025) augment agents with a persistent store managed by explicit add, update, and prune opera- tions, yielding gains on dialogue and planning tasks.
C LIMITATIONS
We show that PolySkill also works for the latest agentic models for continual learning, which is way continuously improve these models with environment interaction settings. This also directly answers the claim that open-source models still fall behind in terms of skill induction (Shah et al., 2025). The promising next step would be to enable smaller agentic models to also utilize such skill acquisition behaviors, via further RI. training on these memory environments.
D SKILL EXAMPLES
We show the ASI and SkillWeaver Skill Examples at Table 4; and the examples for PolySkill is at Table 1.
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E EXPERIMENT DETAILS
E. FORMAL DEFINITIONS OF EVALUATION METRICS
Task Success Rate (SR) This is the fraction of tasks in the evaluation set, Teaa that the agent completed successfully. It is defined as:
SR
Ttaakk 7j successful)
|Teest
23
where I(-) is the indicator function which returns if task is successful and 0 otherwise.
Number of Steps This is the average number of actions the agent takes to complete a task, calculated exclusively over successful trajectories to measure efficiency. Let success. be the set of trajectories for successfully completed tasks. For each trajectory E Puucess: let [ denote its length (number of actions). The average number of steps is:
1
Number of Steps
DD succosss
TED
Skill Reusability This metric measures the efficiency of the skill library itself by calculating the fraction of learned skills that were used at least once. Let AC the final library of learned skills and Diea be the set of all trajectories from the evaluation. The utilization is the fraction of skills k K. that appear in at least one trajectory r E Dest
1( E K Br E Dieat E rH
Skill Reusability
Skill Adoption Rate This metric measures the prevalence of skill-based behavior. Let Diest be the set of all test task trajectories and IC be the library of induced skills. The adoption rate is the fraction of trajectories T E Dees which least one skill k K. was invoked:
HT Diest 3k K, K
Skill Adoption Rate
[Drest
Skill Compositionality This metric evaluates the hierarchical structure of the skill library. Let the final skill library be an ordered set K. {ki....,ke kN} of skills, where the index indicates creation time. For each skill k, let body(k;) be the set of non- non-primitive actions in its implementation. The compositionality is the average number of previously learned skills reused in each new skill:
N
Skill Compositionality
Σ =1
[k E body(k:) 1j iH
E.2 ACTION SPACE
E.3 DATASET
Mind2Web (Deng et al., 2023) is large-scale, comprehensive benchmark designed for the devel- opment and evaluation of generalist web agents. It aims to measure an agent's ability to follow natural language instructions perform complex tasks on any given website. The dataset is notable for its breadth, containing over 2,350 tasks spread across 137 different websites and spanning 31 distinct domains. The tasks are collected from real-world use cases and represent a wide array of common user activities, such as booking flights, managing online shopping carts, and configuring account settings. Each task instance consists a high-level natural language instruction paired with specific website. An agent's 's performance is evaluated based on its ability to successfully complete the instruction by generating a correct sequence web-based actions. like clicking buttons, typing text. and selecting options from dropdowns. This benchmark's 's diversity in both tasks and domains makes a robust tool for training and testing the generalization capabilities of autonomous web agents.
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Category
Action Type
Description
noop
Do nothing
click (elem)
Click at element
hover (elem)
Hover on an element
Basic Actions
type (elem, text)
Type to an element
press (key.comb)
Press key combination
scroll (dir)
Scroll up and down
tab focus (index)
Focus on the i-th tab
Tab Operations
new_tab
Open new tab
tab close
Close current tab
go_back
Visit the last URL
Page Operations
go.forward
Undo go_back
goto (URL)
Go to URL
Table 5: The base primitive action space from BrowserGym (de Chezelles et al., 2025)
WebArena (Zhou et al., 2024a) is a realistic and reproducible web environment and benchmark designed evaluate the functional capabilities of autonomous language agents complex, goal- oriented scenarios. It features 811 distinct tasks that require agents to interact with five fully func- tional, self-hosted web applications: an e-commerce platform (OnnStopShop), social forum (Red- dit), collaborative software development platform (GitLab), map service (OpenStreetMap), and site administration dashboard for the e-commerce platform. Unlike benchmarks that focus on single-site interactions, many WebArena tasks are compositional, requiring the agent to navigate and synthesize information across multiple applications to achieve a final goal.
E,4
REAL--WOLLA WEBSITE TASKS
Amazon (50 tasks)
Search for an "ergonomic office chair" from the brand 'Herman Miller' that costs between $500 and $1000.
Find "27-inch 4k 144hz gaming monitors" that are eligible for Prime shipping.
Look for "air fryer toaster oven combo" with least 20-quart capacity and a customer rating of stars or higher.
Search for new "Sony WH-1000XM5 headphones" in the color 'Silver'.
Find "silicone pet grooming gloves" designed specifically for cats with long hair.
Search for 'Apple' "laptops" with least 16GB of RAM and sort them by price from high to low.
Find "robotic vacuums" from the brand 'iRobot' with self-emptying feature, and sort the results by average customer review.
Search for 'Vitamix" "blenders" with least 1200 watts of power and sort them by price from low to high.
Look for "science fiction books" by author Andy Weir' in paperback format and sort them by publication date, with newest first.
Search for waterproof "men's 's winter jackets" in size 'Large' and sort by newest arrivals.
Find "android tablets" with at least 64GB of storage that cost less than $200.
Show me "men's 's running shoes" from the brand 'Brooks' in size 10.5.
Find programmable "coffee makers" under $75 that have a 4-star rating or higher.
Look for "QLED 4K televisions" from the brand 'Samsung" with a screen size '65 inches'.
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Find waterproof "women's hiking boots" from the brand 'Merrell' in size 8 that are eligible for Prime shipping.
Search for "Whey Isolate protein powder". filter by the flavor "Unflavored", and a customer rating of 4 stars and up.
Find "board games" suitable for "2 players" and for ages "12 and up" with an average customer review of at least 4 stars.
Look for color-changing "smart home light bulbs" that are compatible with "Amazon Alexa".
Find the cheapest "ITB NVMe SSD" with minimum read speed of 5,000MB/s.
From the brand 'Ninja', show me the least expensive "air fryer" with minimum capacity
quarts.
Find the current price and the screen size in inches of the "Kindle Paperwhite (16 GBY".
Go to the product page for the "Instant Pot Duo 7-in-1" and find both its capacity in quarts and its item weight.
On the product page for the "Anker PowerCore 10000" power bank, find reviewers who mention the word "travel" and list their names along with the star rating they gave.
For the "Sony WH-1000XMS headphones, summarize the main criticisms mentioned in the 1-star and 2-star reviews.
For the "Breville Barista Express Espresso Machine", find positive customer reviews (4- stars or higher) that specifically talk about the "steam wand".
Summarize the positive comments from 5-star reviews for the "Kindle Scribe" that specif- ically mention the "writing experience".
Summarize what eustomers in 3-star reviews say about the battery life of the "Apple Air- Pods Pro (2nd Generation)".
Find the product page for the book "Dune" by Frank Herbert, list the available formats, and find the price of the Mass Market Paperback edition.
Check the product page for the "Logitech MX Master 3S" mouse see if it is compatible with both macOS and Windows 11.
Find the "LEGO Star Wars Millennium Falcon" (model 75257) and list its item dimen- sions and the manufacturer's 's recommended age range.
Add two "Echo Dot (5th Gen)" devices in the color 'Charcoal' to my shopping cart.
Find "Hydro Flask 32 oz" Wide Mouth bottle in the color 'Olive' with Flex Straw Cap and add it to the cart.
Add two "Anker USB C to Lightning" cables (6ft) in 'White' to the shopping cart.
Find the book "The Hobbit" by J.R.R. Tolkien and add both the paperback and the Kindle versions to your cart.
Add one "The Lord the Rings" paperback box set to the cart, view the cart, and then update the quantity two.
Add the "Breville Barista Express Espresso Machine" (model BES870XL) to my default wish list.
Find the "Catan Seafarers" board game expansion and add it to your wish list.
Add the "Sony WH-1000XM5" headphones in black to your wish list, then navigate to your wish list and sort by "Price: High to Low".
Create a new, private wish list named "Tech Gadgets".
Add "Samsung 980 Pro 2TB SSD" to the "Tech Gadgets" wish list with the comment "For new PC build".
Navigate the "Today's Deals" section and then filter to see only deals the "Electronics" category.
Go to the "Best Sellers in Books" page and then navigate to the "Seience Fiction & Fantasy" sub-category.
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Show me the new releases in the "Electronics" category from the "Last 30 days".
Go to the Amazon "Gift Cards" page and then find the "eGift Cards" section.
Navigate to the customer service section and find help related to "A delivery, order or return".
Find Amazon's 's return policy page and determine the return window for new television.
Find the help page on tracking packages and identify what to do if the tracking information is not updating.
Navigate the "Coupons" section of the website and filter to see coupons for "Grocery & Gourmet" products.
Find "Amazon Basics" products the "Home & Kitchen" category and sort the results by newest arrivals.
Add "Corsair K70 RGB PRO Mechanical Gaming Keyboard" with 'Cherry MX Red' switches to the cart and then proceed to the checkout page.
Target (50 tasks)
Search for "ergonomic office chair" from the brand 'Threshold' that costs between $100 and $250.
Find "27-inch 4K computer monitors" that are available for same-day delivery.
Look for "air fryer toaster combo" with at least 20-quart capacity and guest rating of stars or higher.
Search for new "Beats Studio Pro headphones" in the color 'Navy".
Find "kitchen towels" from the brand 'Hearth Hand with Magnolia" that are 100% cotton.
Search for "laptops" with at least 16GB of RAM from the brand 'HP" and sort them by price from high to low.
Find "robotic vacuums" from the brand 'Robott' with self-emptying feature, and sort the results by "Best sellers".
Search for "single-serve coffee makers" from the brand 'Keurig' and sort them by price from low to high.
Look for "LEGO Creator 3-in-1" sets and sort them by "Newest".
Search for "men's 's winter jackets" that are waterproof and in size 'Large', then sort by guest rating.
Find "patio conversation sets" that include a fire pit and cost less than $750.
Show me "women's 's athletic shorts" from the brand 'All in Motion' in size 'Medium'.
Find "electric toothbrushes" from the brand Philipps Sonicare' with guest rating of 4 stars or higher.
Look for "OLED 4K smart TVs" from the brand 'Sony" with a screen size of '65 inches", and filter for "Order Pickup".
Find "men's ' hiking boots" from the brand 'Merrell' in size 10 that are available for "Same Day Delivery".
Search for "blenders". filter for the 'Ninja' brand, models with Auto-iQ, and guest rating of 5 stars.
Find "nursery gliders" from the brand DDaiinc that are currently on sale and available in the color 'Gray..
Look for "Nintendo Switch" games that are rated "Everyone 10+" and have the "Action" genre selected.
Find the cheapest "8-cube organizer shelf" from the brand 'Brightroom' available in 'Whtee'.
Show me the least expensive "carry-on luggage" that has a hardside exterior, spinner wheels, and is from the brand 'Made By Design'.
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Find the current price and overall dimensions of the "Threshold designed with Studio McGee Herriman Wooden Console Table".
Go to the product page for the "Keurig K-Mini Single-Serve Coffee Maker" and find it has an auto shut-off feature and its water tank capacity from the item details.
On the product page for "Good Gather Organic Blue Corn Tortilla Chips", check the nutrition details
the amount of sodium and dietary fiber per serving.
For the "Beats Studio Pro" headphones, go to the Q&A section and find out the reported battery life and they come with carrying case.
Find customer reviews for the "Dyson V8 Origin Cordless Stick Vacuum" that mention its performance on both "pet hair" and "hardwood floors".
Summarize the positive comments from 5-star reviews for the "Ninja Foodi 6-in-1 8qt 2- Basket Air Fryer" that talk about "ease of cleaning" and "cooking speed".
What do customers in the 1- and 2-star reviews say about the fit and fabric quality of the "A New Day Women's High-Rise Slim Fit Ankle Jeans"?
Find the product page for the book "Fourth Wing" by Rebecca Yarros. What is the listed genre and the total number of pages in the item details?
Check the product page for the "Apple iPad 10.9-inch (10th Generation)" to see how many colors it is available in and if it is compatible with the 1st generation Apple Pencil.
Find the "Fisher-Priee Price Laugh Learn Smart Stages Piggy Bank" toy and identify the manu- facturer's suggested age range and the number of batteries required from the product details.
Add two cartons of "Good Gather Grade A Large Eggs 12ct" to my shopping cart.
Find a "Stanley 40oz Stainless Steel H2.0 FlowState Quencher Tumbler" in the color 'Fog' and add it to the cart, then change the quantity 2.
Add two "up up 50-load srrendr--cceted laundry detergent" containers and one "downy fabric softener" to the shopping cart.
Find "Hearth Hand with Magnolia" brand scented candle and add two different scents to the cart.
Add one bag of "Good Gather Organic Baby Carrots" and one "Good Gather Classic Hummus" to the cart, then proceed to checkout but do not place the order.
Create new baby registry for an expected arrival date of June 1, 2026, and set it to be private.
After creating baby registry, add both the "Graco 4Ever DIXX 4-in-1 Convertible Car Seat" and the "Owlet Dream Sock Baby Monitor" to it.
Create a new custom list named "College Dorm Essentials".
Add "Room Essentials Twin/Twin XL Microfiber Comforter" in gray your "College Dorm Essentials" list.
Add a "Brightroom Tier Metal Utility Cart" in white to your "College Dorm Essentials" list with a note that says "For bathroom and shower supplies".
Navigate to the "Weekly Ad" section and find a deal on "Good Gather" brand ground beef.
Go to the "Clearance" section of the website and filter for "Home Deals" that are discounted by 50% more.
Find the "Target Circle" offers page and clip a deal for "20
Go to the "Gift Cards" page and find the section for "Thank You" themed e-gift cards.
Find the store locator page and check the guest service hours and Starbucks hours for the Target store in Somerville, MA.
Find information on Target's 's return policy for electronics that have been opened and used.
Navigate the "RedCard" page and find the listed benefits having Reloadable RedCard versus a Debit or Credit RedCard.
From the homepage, navigate to the "Toys" category and filter for "Action Figures Play- sets" from the brand "Marvel".
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Find the "Top Deals" in the "Home" category and sort them by "Discount: High-low".
Add "Threshold 16pc Stoneware Avesta Dinnerware Set" to your cart, then proceed to checkout and select "Store Pickup" for the Medford, MA location.
Github (75 tasks) Note that the GitHub tasks, we force the tasks be navigational or tasks that won't codeify existing code. We created dummy account for performing the experiments.
Go to your profile and change your bio to "Building the future, one line of code at a time".
Set your personal website URL in your profile to "https:/www.giihbb.cm Set your current status to "Focusing on a project" with the busy indicator on.
Find the "microsoft/vscodde" repository and star it.
Create a new, public repository named "learning-python".
Create a new, private repository named "project-secress" and initialize it with a README file.
Create new public repository named "website-template from the "jekyll/jekyll-now" template.
Fork the "facebook/react" repository to your own account.
In your "learning- pptthnn""pt""" repository, create a new file named "hello.py" with the content "print(" (Hello, World!""
Edit the README. file in your (efearning-pythonn repository add the description "My personal repository for learning Python".
In your Shaaming--ythooi repository, change the LICENSE file to the "MIT License".
Find the SSH URL to clone the "tensorfoow/tensrfooow" repository.
Navigate to the "twbs/bootstrap" repository and view its commit history for the "main" branch.
In the "torvalds/linux" repository, find out how many commits were made by "Linus Tor- valds" in the last month.
Find who the top contributor is for the "openai/gpt-3" repository.
List the names and number commits for the top 3 contributors to the "google/gvisor" repository.
Go to the "Explore" page to see trending repositories.
Search for repositories with the topic "web-agent" written in the "Python" language.
Find and navigate to your notifications page.
Go to the page that lists all pull requests that are assigned to you.
Navigate the page that lists all issues where your review is requested.
Find the "Xubernetes/uueemettts"" repository and view all its open issues.
In the "microsofttterminaa"" repository, filter the issues to show only those with the "bug" label.
Create a new issue in your Shamming-pythone repository with the title "Need to add a re- quirements.txt file".
In your Seaaring--yyhooee repository, create issue titled "Refactor hello.py" and sign it to yourself.
Create an issue in your "learning "learning-python" repository with the title "Add data structures examples", and add the labels "enhancement" and "help wanted".
In the "atom/atom" repository, find the oldest open issue and leave the comment "Is this still relevant?".
Find a pull request in the "expressjs/express" repository that updates dependencies and post the comment "LGTM!" on it.
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In your "website-temple template" repository, create branch named "feature/add-contacte page".
Create pull request in your "website-template repository to merge the "feature/add- contact-page" branch into the "main" branch.
Create a pull request in your "website-template" repository with the title "Update Home- page", merging from "develop" to "main", and assign yourself as the reviewer.
In your (leerming-pythowe repository, add "torvalds" collaborator with "Read"" access.
Create a new milestone in your "learning-pythow" repository titled "Version 1.0 Release".
Set the due date for the "Version 1.0 Release" milestone your "leaming-pyython" reposi- tory be one month from today.
Create an issue titled "Finalize documentation" in your (leaninggpythhnr repository and assign it to the "Version 1.0 Release" milestone.
Find all closed issues the "docker/compose" repository that mention "network".
View your starred repositories and sort them by "Recently starred".
Create a new organization named Myy-Awesome-SSatup-OOrr""
Invite the user "octocal" to be a member of your "My-Awesome--Strtup-OP oraarsa
Find the "sveltejs/svelte" reppeeissseelle""rpotttrr and navigate its "Projects" board.
In the "NttionaSScurrtyAgenn/ghhhhh repository, find the total number of watchers. repps
Change the description of your "learning-python" repository to "A repository to track my Python learning journey and projects".
Enable GitHub Pages for your "website-tepplate" repository on the "main" branch.
In the "numpp/numpy" repository. find the pull request with the most comments.
In your "learning (tearning-pythort repository, protect the "main" branch to require a pull request review before merging.
Find the "actions/checkout" repository and view its different tags/releases.
Create new private repository and import the "git/git" repository into it.
Follow the user "Linus Torvalds" (torvalds) on GitHub.
In the "rust-lang/rust" repository, find all issues with the "A-async-await" label that are currently open.
Create pull request in your "learning "eearnin--python" python" repository from new branch called "fixtypo-in-readmee" to "main" that corrects spelling mistake in the README.
E.5 PROMPTS
Skill Induction Prompt We follow similar prompts as the ASI paper (Wang al., 2025).
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Prompt for Skill Induction
You are a proficient software engineer. Your task is (1) summarize reusable functions as APIs from the provided action trajectories, and (2) rewrite the trajecoties using the reusable functions you generated (1).
Tasks: (task)
Domains: [domain_url)
Trajectories: Planner Step Executor Step 1
Planner Step 2
Executor Step 2
Planner Step 3
Executor Step 3
For (1), from the provided examples about the same task, you job is to generate Python func- tions that can be reused solve (part of) these tasks. The functions should have mediocre complexity: (i) containing least three actions and not too simple (e.g., single line of code), (ii) not too complex (e.g., more than lines of code), and should be general enough to be applied to other similar tasks. The arguments these functions should be common variables (such as strings and lists), avoid using complex inputs such as another function. Please include 'Args', 'Returns', and 'Examples' in the function documentation. For (2), write the instruction and rewritten code of each example. Do not include the answer response example-specific information in the rewritten code. Pay attention whether all link IDs are available before specifying them in the generated functions. If you use "send msg oo_user', make sure the message is decided within the function, instead of pro- vided as an argument.
Make sure each function contains no less than 2 steps, and no more than steps; to keep the functions simple and task-oriented. You can generate zero, one, or multiple functions depending on the provided examples.
Judge Prompt For Judge, we follow the prompt used in Online-Mind2Web (Xue et al., 2025), which showed over 85% agreement with human annotators.
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WehJudge Key Point Identification
You expert tasked with analyzing given task identify the key points explicitly stated in the task description.
=#Ojjettive#:; Carefully analyze the task description and extract the critical elements explicitly mentioned the task for achieving its goal.
** *#Instructions*>
1. Read the task description carefully.
2. Identify and extract **key points** directly stated in the task description.
A **key point** is critical element, condition, or step explicitly mentioned in the task description.
Do not infer or add any unstated elements.
Words such as "best," "highest," "cheapest," "latest," "most recent," "lowest," " "closest," "highest- rated," "largest," and "newest" must through the sort function (e.g.. the key point should be "Filter
by highest").
** Respoond with**;
**Key Points**; A numbered list the explicit key points for completing this task, one per line, without explanations or additional details.
Task: {task)
WebJudge Key Screenshot Identification
You expert evaluator tasked with determining whether an image contains information about the necessary steps to complete a task.
**Ojeetive Analyze the provided image and decide if it shows essential steps evidence required for completing the task. Use your reasoning explain your decision before assigning score.
IInstructions**
1. Provide detailed description of the image, including its contents, visible elements, text (if any), and any notable features.
Carefully examine the image and evaluate whether it contains necessary steps or evidence crucial to task completion:
Ideatify key points that could be relevant to task completion, such as actions, progress indicators, tool usage, applied filters, step-by-step instructions.
Does the image show actions, progress indicators, or critical information directly related to completing the task?
Is this information indispensable for understanding or ensuring task success? If the image contains partial but relevant information, consider its usefulness rather than dismissing it outright.
Provide your response in the following format: ### Reasoning: (Your explanation]
### Score: [1-5]
**Task**; (task)
**Key Points for Task Completion {key points)
The snapshot of web page shown image.
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WehJudge- - Outcome Judgement
You are an expert in evaluating the performance of web navigation agent. The agent is designed to help a human user navigate a website complete task. Given the user's task, the agent's 's action history, key points for task completion, some potentially important web pages in the agent's 's trajectory and their reasons, your goal is to determine whether the agent has completed the task and achieved all requirements.
Your response must strictly follow the following evaluation criteria!
'Important Evaluation Criteria*:
I: The filtered results must be displayed correctly. filters were properly applied (i.e., missing selection, missing confirmation, or no visible effect in results), should be considered a failure. 2: You must carefully check whether these suapshots and action history meet these key points. Ensure that specific filter conditions, such as "best," "highest," "cheapest," "latest," "most recent," "lowest," "closest," "highest-rated." "largest." and "newest" are correctly applied using the filter function (e.g.. function).
3: Certain key points or requirements should applied by the filter. Otherwise, a search with all requirements as input will be deemed a failure since it cannot guarantee that all results meet the requirements!
4: If the task requires filtering by specific range of money, years, the number of beds and bathrooms, applied filter must exactly match the given requirement. Any deviation results in failure. To ensure the task is successful, the applied filter must precisely match the specified range without being too broad or too narrow.
5: Some tasks require submission action display of results to be considered successful. Repeat actions actions that do not lead visible result should be considered failure. If the agent loops through a sequence of actions that do not make progress toward the goal (including failing to click "Save" or "Submit,"" etc.), it should be considered failure.
Format your response into two lines shown below: Thoughts: iyour thoughts reasoning process based on double-checking each key points and the evaluation criteria,
Status: "success" or "failure"
User Task: {task}
Key Points: (key points}
Action History: {action history)
The potentially important snapshots of the webpage in the agent's trajectory and their reasons: (thoughts}
COMPLETE RESULTS
We put our complete results at Table 6 and Table 8.
Table 6: Performance comparison on Mind2Web benchmark. Results show success rates (%) across different generalization scenarios. Green indicates improvement and red indicates degradation from baseline. Best results in bold.
GPT-4.1 (Training: 1009 tasles)
Claude-A7 (Training: 1009 tasks)
Method
Cross-task (252)
Cross. Wehsite (177)
Cross- Drasi-Dmmain (912)
Cross-tade -ask
Cross. Website
Cross Domain
Baseline
53.8
56.2
ASI (All skills)
52.3 (-1.5)
54.9ana (-1.3)
37.8 (-2.9)
60.3g1. (+1.2)
61.8 (40.4)
66.9.1. (+0.7)
ASI (Same domain)
55.2g14 (+1.4)
57.0g1 (+0.8)
NIA
60.9114 (+1.8)
6488 (+0.4)
NIA
ASI (Same sub-domain)
56.3,17 (12.5)
NiX
NIA
61.2 (12.1)
N/A
NA
(+Update)
50.4g21 (+5.6)
58.7gra (+2.5)
62. 1.9)
62. 62. (+3.0)
65.1gr (10.7)
67.3 (+1.))
PolySkill (All Skills)
55-4g1a (+1.6)
57.6411 (+1.4)
60.1a (-D.1)
61.349. (42.2)
64.920 (40.5)
66 4a-s (+0.2)
PolySkill (Same domain)
58-3=18 (44.5)
58.941a (+2.7)
N/A
62.041a (42.9)
64.5aa (40.5)
N/A
PolySkill sub-dom.)
58.6.20 (+18)
N/A
02.311. (+3.2)
PolySkill (+Update)
63.2g2.4 (49.4)
61.3 (+5.1)
63.422,7 (43.2)
64.6 (+5.5)
66.21. (+1.8)
68.3 (+2.1)
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Bascline
SkillWeaver
ASI
PolySkill
GPT-4.1
Claude-3.-Soonet
Success Rate (%)
60
60
40-
40
20
0-
Average ShoppingAddiin Reddit
Map
Cross
Avenuge Shopping
Map
Bwen3-Cooden4888-A333B
7
GLM-4,5
80
Success Rate (96
60
40
40
20
Average
Shopping,Auim Admin Roddit
Gillab
Map
Croes
Average
Shopping Admin Reddit
Girlab
Map
Cross
Figure 6: Overall performance comparison of PolySkill with baselines on the WebArena bench- mark across four leading language models. The x-axis shows different website categories, with the leftmost Average group representing the primary overall result. PolySkill consistently achieves the highest average success rate all models, demonstrating its effectiveness on complex, realistic web tasks. Notably, our method surpasses strong skill-learnng baselines like ASI and Skil/Weaver, with the most significant gains observed on the powerful GPT-4.1 and Claude-3.7 -oaude-..--Sone models.
Table 7:
Mind2Web results for open-source models. These represent the first evaluation of skill learning methods on open-source agentic models.
Qwenn--CoderfROR-- (Training: 1009 tasks)
GLM-4.5 (Training: 1009 tasks)
Method
Cross-task (252)
Cross: Website (177)
Cross Mous-Domain (912)
Cross task
Cross Website
Cross Domain
Baseline
40.8
38.1
37.5
44.8
44.2
43.7
ASI
41.5gr.a (+0.7)
36.4 (-1.7)
35.2 (+2,3)
45.21oa (+0.4)
44.5.o0 (40.3)
42.6 (-L.)
ASI (+Lpdatt)
44.92a.
40.1 (+2.0)
38.7 (+1.2)
47.0g4 (+2.2)
46.24a7 (+2.0)
45.1 (+1.4)
PolySkill
42.3 42.1g1 (+1.5)
37.2 (-0.9)
35.9g1 (-1.6)
46.1100 (+1.3)
44.7.0s (+0.5)
42.9g (-0.8
(+Updaal
47.5 (+6.7)
41.824.
39.944 (+2.4)
49.2447 (+4.4)
47.1244 (42.9)
46.024s (+2.3)
F.1 COST AND EFFICIENCY ANALYSIS
To evaluate the economic feasibility and efficiency of framework, we conducted detailed cost analysis measuring token consumption. We calculated the average token usage (input output) per instance in the Mind2Web cross-task setting using G.T-4.1. The breakdown distinguishes between the Action Generation phase (inference) and the overhead costs associated with Verification and Skill Induction.
As shown in Table 10, PolySkill introduces an overhead for skill induction and verification, resulting in a higher total token count for the initial run compared the no-skill baseline. However, this investment yields significant efficiency gains:
1. Reduced Inference Cost: PolySkill reduces token consumption during the Action Gener- ation phase by approximately 10.2% compared the Baseline (from ~55.4k 4<4..7k tokens) and 7.7% compared to ASI. This is attributed to the polymorphic skills allowing the agent solve tasks in fewer steps with higher precision.
2. Amortized Efficiency: Crucially, the costs for Verification and Skill Induction pri- marily one-time (or amortized) costs. Once a concrete implementation for website is induced, it stored in the library and reused for all future tasks that site. Conse-
27

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Published as a conference paper at ICLR 2026
Table Performance comparison WebArena benchmark. Results show success rates (%) across
different website categories. Best results in bold, second best underlined. Experiments with are pending completion.
Method
Shopping 187
Admin 182
Reddit
Gitlab 180
Map 109
Cross 48
Average 812
# Instances
106
GPT-4.1
Baseline
37.4
44.0
66.0
38.9
16.4
10.3
38.5
Seaver
39.3
48.2
71.2
50.3
17.2
16.3
43.6
ASI
46.3
53.6
73.7
46.8
21.5
15.1
46.5
PolySkill (Ours)
51.4
54.8
73.2
54.2
18.9
18.9
49.3
VS ASI
+5.1
+1.2
-0.5
+7.4
-2.6
+3.8
+2.8
Claude-3 3.7-Sonnet
Baseline
44.7
50.8
70.2
46.7
18.3
12.1
45.6
SkillWeaver
46.2
52.7
72.5
55.1
19.1
16.2
47.5
ASI
59.1
61.3
78.5
64.2
26.7
20.1
55.8
PolySkill (Ours)
58.3
63.5
80.4
62.5
27.4
25.6
59.5
VS ASI
-0.8
+2.2
+1.9
-1.7
+0.7
+5.5
+3.7
Qwen3- Seinnn--odee-44400 4800--338BBBB-B B--BB-33B--nnnrrre Instruct
Baseline
28.1
31.2
52.4
27.7
10.5
6.8
34.4
Skaver
31.3
36.1
58.7
38.5
12.7
11.4
38.1
ASI
45.1
44.8
63.5
47.2
16.7
13.1
43.9
PolySkill (Ours)
44.4
46.3
64.2
46.1
17.1
16.8
45,2
VS ASI
-0.7
+1.5
+0.7
-1.1
+0.4
+3.7
+1.3
GLM-4.5
Baseline
36.1
40.9
59.4
37.5
15.7
10.0
36.2
SkillWeaver
34.2
39.7
54.8
39.1
16.4
11.2
33.6
ASI
38.6
43.7
62.5
40.8
17.3
12.1
38.9
PolySkill (Ours)
39.4
44.5
63.2
41.6
17.8
13.0
39.8
vS ASI
+0.8
+0.8
+0.7
+0.8
+0.5
+0.9
+0,9
quently, the number of tasks performed website increases, the average cost per task converges toward the significantly lower Action Generation cost, making PolySkill highly cost-effective for long-term term deployment.
G EXTENDED METHODOLOGY
28

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Published as a conference paper at ICLR 2026
Training Setting
GitLab
Github
Method
Iters
SR %
Skill Usage %
SR %
Skill Usage %
Bascline
38.9
66.7
1. SSinne- Specialists
GitLab
50
65.5
59.2
71.5
12.8
Github
50
40.2
3.8
81.5
54.1
2. Sequential Curriculum
Gitlab Github 50 + 50 50 +
48.3
20.5
80.1
51.9
Github - GitLab
62.1
45.3
77.8
48.6
3. Self-guided Exploration
Github + GitLab
100
66.2
48.1
84.0
39.5
Table Skill transfer results between software development platforms. This experiment highlights the challenge of transferring skills from a higher-performing domain (GitHub) to more complex one (GitLab). Self-guided exploration, which learns from both domains concurrently, achieves the highest success rate on the held-out GitLab benchmark.
Table 10: Average token consumption per task instance on Mind2Web. PolySkill significantly re- duces token usage during action generation, leading to lower long-ten long-term costs despite the initial overhead of skill induction.
Method
Action Gen.
Verification
Skill Induction
Total Tokens
Baseline (No Skills)
55,367.4 53,840.5
55,367.4 64,287.5
ASI
4,673.8 4,963.2
5,773.2 8,476.4
PolySkill
49,710.3
63, 63.149.9
Algorithm PolySkill in a Task Setting (illustrating changes from Algorithm
1: Input: A sequence of tasks Q = {q1..... qN ) LM Policy Tg. LM Judge Ve
2: Input: Number of exploration steps T, LM Policy me, Auto Judge Ve
3: Initialize: Dynamic skill library Ko 0
4: + Initialize: Dynamic skill library Koo + 0, Initial observation oo
5: -fort t 1,..., NN do
6:
+for ..... T do
8:
Let qe be the current task from Q. gproposed f- ProposeTaske TanosseTTs(TTTt RC, Ot-1, KG-1) Define the agent's full action space: At t- Ap U UK-11
10:
+
11:
12:
Execute Task
ExecuteTask((Tg (T,s qe,
+
<- TasektteTask(ff mC, Oproposed: At)
14:
Ve(TT, gr) then
16:
+ if Vc(T, (proposed) then
18:
knew + InduceSkill(T,,TT T, KG-1)
K₁+ KK- {knew)
20:
21:
KGt
22:
23:
+
0, f- GetLastObservation(17
24:
25:
29


Reviews:
[{"review_id": "boimPFJuAY", "reviewer": "Reviewer_hnAi", "summary": "This paper proposes a method that improves the reusability of skill induction methods for agents by encouraging the agent to create abstractable skills using polymorphism.", "questions": "One thing that was not very clear to me (sorry if I missed this) -- is the abstract class only created once when the web site is first processed? Or can it be modified later as more evidence about the web site comes to light?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The method is simple but strikes at the core of the problem.\n2. The motivation and description is very clear, illustrated by appropriate examples.\n3. The paper compares across multiple datasets against strong baselines from the recent literature.", "weaknesses": "1. As mentioned in the future work section, it is less clear how this method will be applicable in cases where task category boundaries are more fuzzy. The hard-coded task boundaries required to induce new abstract classes may make this tricky. I don't view this as a critical weakness of the paper though, more of an opportunity for future work.", "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": "bUTeKh4NMD", "reviewer": "Reviewer_kozN", "summary": "The paper proposes PolySkill, a polymorphism-guided framework for skill induction in web agents. A skill is defined by an abstract interface capturing semantic intent, linked to multiple interchangeable concrete implementations across websites. This separation aims to improve transfer, modularity, and recomposition of skills, and to support continual learning and adaptive behavior in task-free settings. The authors also introduce process-level metrics—Skill Reusability, Task Coverage, and Skill Compositionality—to assess what the agent actually learns beyond task success.", "questions": "1. How is a domain’s abstract class discovered, manually seeded, or fully induced? What prevents over-abstracting or under-abstracting? \n2. How is the $\\gamma$ penalty on steps set; is it tuned per model/domain; sensitivity?\n3. What is the compute & API cost of induction/verification vs. Baselines?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Skills are specified by an abstract interface plus interchangeable concrete implementations, enabling cross-site reuse and composition while insulating from UI changes. \n2. A three-stage flow consisting of polymorphic abstraction, compositional verification, and adaptive execution clarifies how skills are discovered, validated, and deployed.", "weaknesses": "1. It’s unclear how much gain comes from the interface abstraction vs. better verification, task curricula, retrieval, or engineering heuristics.\n2. Most compelling examples are shopping; results on other domains (dev tools) are thinner and may conflate prior knowledge with schema 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": "9RSzaZ5sfR", "reviewer": "Reviewer_dJWv", "summary": "The paper presents a framework in which an LLM agent learns skills that can be reused across websites (e.g., \"search\", \"add_to_cart\", \"checkout\"). At the core of this framework is the separation of \"what\" from \"how\" (e.g., the framework defines an abstract shopping website class that is specialized to different actual shopping websites). Experiments compare the performance of the proposed system with prior work on existing benchmarks.", "questions": "1. Can the abstract class definition be inferred from the initial successful traces?\n2. Can the system accidentally use a not-yet-implemented skill in its implementation of a skill?\n3. How are the \"+Update\" systems implemented?\n4. Please consider running the experiments providing the information corresponding to the abstract class to the baseline systems that allow it. This would further allow us to understand if the performance benefits come simply from the apriori identification of relevant skills or actually from the polymorphic skills.\n5. From the manuscript, I am guessing the +Update and +Online systems modify their implementation of the concrete classes. Is this corrrect? Or do these systems also update the weights of the LLMs? Please include a description of these systems in the main text.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper clearly positions the proposed apprpoach relative to prior work (ASI), making it easy to understand the technical contribution.\n- The paper cleverly uses polymorphism, a well-understood concept from classic software engineering principles, to improve generalizability in LLM agents (although I am not an expert in this area, and thus I cannot be certain of my assessment of novelty).", "weaknesses": "- The abstract class that specifies the relevant skills is provided as context. This is a very restrictive assumption, as it assumes (1) that the set of useful skills is fixed, (2) that the set of useful skills is known.\n- I found the current description to be insufficient to understand the method, and had to consult the appendix. Please consider improving the framework's pseudocode to the main text, or at least including a precise step-by-step description in the main text.\n- It seems that none of the baseline systems were given the extra information corresponding to the abstract class description (or this was at least not described), which makes the comparison perhaps biased. Giving this information to at least some of the systems should be possible (perhaps simply by including it in the prompt).\n- Some parts of the methodology are underdocumented (see questions 1, 2, 3, 5).", "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": "YvMbi6FCiN", "reviewer": "Reviewer_M5TL", "summary": "This paper proposes a method to induce skills that transfer across websites, by decoupling their abstract goals and concrete implementations. Both experiments on static benchmarks and free-form continual learning settings show the empirical benefits of the proposed method.", "questions": "1. How different are the skill implementations within the same abstract class? Are they mainly different due to the varied designs of targeting websites, or other reasons? How similar (quantitatively) do two skill implementations need to be, in order to sit in the same abstract skill class?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**1. Importance of Skill Transferrability.**\n> This paper tackles a critical limitation of current agent skill learning studies – transferability across websites.\n\n**2. Empirical Improvements throughout Experiments.** \n> The paper evaluates on both (i) static benchmarks and (ii) open-ended exploration settings. The proposed method shows improvements in success rate, efficiency, and skill reusability for (i); as well as greater task coverage and skill compositionality for (ii).", "weaknesses": "**1. Writing Clarity Issue: confusing concepts of “websites” and “domains”.**\n> Different websites do not necessarily associate with in-domain and out-of-domain. Two websites can either belong to the same domain (e.g., both United and Spirit Airlines are for traveling) or not (e.g., Amazon for shopping and United for traveling). That being said, this paper aims to induce skills that transfer across websites, but it is unclear (at least from the intro) if these are websites within the same domain or across different domains.\nFurther, from the experiments, the proposed method only works for websites in-domain, so claiming its effectiveness in out-of-domain scenarios lacks evidence.\n\n**2. Missing Skill Count Measures.** \n> While the success rate and efficiency gains are promising, the paper fails to report the number of skills (abstract class and specialized implementations). Ensuring a small number of skills is important as it avoids redundancy in the skill library. The very low number of steps introduces concerns that there may be one skill unnecessarily targeting each specific example, instead of trying to capture shared similarities across examples. Reporting this dimension, similar to Table 3 in [1,] would help address this concern.\n\n**3. Minor**\n> but better remove the vspace before the “Conclusion” section title.\n\nOnce these missing aspects are added to the revised version, I am happy to increase the scores.\n\n[1] Wang, Zhiruo, Daniel Fried, and Graham Neubig. \"Trove: Inducing verifiable and efficient toolboxes for solving programmatic tasks.\" arXiv preprint arXiv:2401.12869 (2024).", "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)