Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning

Spreadsheet-RL introduces a novel RL fine-tuning framework with concrete, measurable improvements on practical tasks, a new benchmark dataset, and a training environment (Spreadsheet Gym) that enables reproducible research. Its real-world applicability to ubiquitous spreadsheet workflows gives it broad impact potential. While AttuneBench addresses an important gap in emotional intelligence evaluation with a thoughtful methodology, it is primarily a benchmark/evaluation contribution without a training methodology. Spreadsheet-RL's combination of framework, benchmark, and demonstrated performance gains across general and domain-specific tasks suggests wider methodological influence and practical adoption.

Paper 2 likely has higher impact: it targets a ubiquitous real-world platform (Excel) with broad cross-industry utility, introduces an RL fine-tuning framework plus a realistic environment (Spreadsheet Gym) and a new benchmark/dataset, enabling reproducible progress and follow-on work. The methodological contribution (RL in a tool-rich, multi-turn setting) is timely for LLM agents and computer-use research, with potential to influence agent evaluation and training beyond spreadsheets. Paper 1 is useful but more niche (story illustration) and largely prompt-layer orchestration without new training or benchmarks at comparable breadth.

Spreadsheet-RL addresses a highly practical and widespread problem (spreadsheet automation) with a novel RL-based framework, including a new benchmark, training environment, and data pipeline. Its real-world applicability to billions of spreadsheet users gives it enormous potential impact. While ST-SimDiff offers a clever training-free video token reduction method, it represents more of an incremental improvement in the already crowded video understanding efficiency space. Spreadsheet-RL opens a relatively underexplored research direction combining RL with domain-specific tool use, with broader implications for LLM-based data interface agents.

Paper 1 is likely to have higher scientific impact due to its methodological contribution (an RL fine-tuning framework plus a realistic Excel “gym”), creation of scalable data/benchmark resources, and clear, measurable performance gains on practical tasks. Its real-world applicability (spreadsheet automation in finance, supply chain, etc.) is immediate and broadly relevant to enterprise workflows and agentic AI. Paper 2 is timely for AI safety and cognitive alignment, but is primarily an evaluation/probing study with narrower direct application and less of a reusable systems/dataset/tooling advance.

ResDreamer introduces a novel hierarchical world model architecture with residual reconstruction for self-supervised visual reasoning in 3D environments—a fundamental contribution to reinforcement learning and world models. Its principled, domain-agnostic design following the 'Bitter Lesson' and demonstrated scalability make it broadly impactful across RL, robotics, and embodied AI. Paper 2, while practically useful for spreadsheet automation, is more application-specific, incremental in its use of RL fine-tuning for LLM agents, and addresses a narrower problem domain with limited broader scientific novelty.

Paper 2 has higher potential impact due to its unprecedented scale (trillion-minute, 5M-participant) self-supervised foundation model for wearable signals, broad validation across 35 clinically relevant tasks, and demonstrated label efficiency—key for a field constrained by annotations. Its applications span major health domains and could influence digital health, clinical decision support, and longitudinal population studies. Methodologically, it combines scaling evidence, downstream head search via LLM agents, and clinician-rated safety/relevance for a Personal Health Agent. Paper 1 is valuable but more domain-specific and likely narrower in cross-field scientific influence.

Spreadsheet-RL addresses a broadly applicable problem (AI-driven spreadsheet automation) with a novel RL-based training framework, new benchmark datasets, and a reproducible gym environment. Its impact spans AI/ML, human-computer interaction, and practical productivity tools used by hundreds of millions. Paper 1 addresses an important but narrower niche (runtime safety assurance using Subjective Logic), with impact largely confined to safety-critical systems engineering. Paper 2's combination of broader applicability, timeliness in the LLM agent space, and concrete performance improvements gives it higher estimated scientific impact.

IdleSpec introduces a novel, broadly applicable inference-time optimization that exploits idle time during LLM agent tool calls—a pervasive but underexplored inefficiency. Its generic, scalable approach with learned drafting strategies applies across diverse agentic scenarios (web browsing, coding, QA), offering broader impact potential. Spreadsheet-RL, while practically useful, addresses a narrower domain (spreadsheet automation) with a more incremental contribution (applying RL fine-tuning to a specific task type). IdleSpec's methodological innovation in speculative planning under uncertainty has wider implications for the growing field of LLM agents.

Paper 1 likely has higher scientific impact due to a concrete, novel RL fine-tuning framework, a realistic Excel-based environment, and new benchmark datasets enabling reproducible progress and direct deployment potential in ubiquitous spreadsheet workflows. It provides measurable performance gains and infrastructure that other researchers can build on across agent learning, tool use, and human-in-the-loop data work. Paper 2 presents an important conceptual safety argument, but as a position paper without empirical validation or implemented artifacts, its near-term impact may be less certain despite high relevance.

Paper 2 introduces a novel framework, a new benchmark dataset, and an interactive environment for LLM agents, offering strong methodological rigor and quantitative results. Its focus on automating ubiquitous spreadsheet tasks gives it immense real-world application potential and broad impact across data-centric fields. In contrast, Paper 1 is a qualitative study with a small sample size, which, while relevant to organizational behavior, offers less foundational infrastructure for future scientific research.

Paper 2 has broader and more durable impact: corpus-level trace diagnostics is a cross-domain problem affecting most LLM agent deployments, and a systematic, evidence-backed diagnostic framework can improve reliability across many tasks, models, and toolchains. Its methodology (hypothesis propose/test over large trace corpora) targets a key bottleneck—debugging and iteration—and shows measurable downstream gains, suggesting strong real-world applicability. Paper 1 is timely and useful but more domain-specific (Excel/spreadsheets) and its RL+benchmark contribution, while solid, is narrower in breadth.

While Paper 1 presents a highly practical application for spreadsheet automation, Paper 2 tackles a fundamental bottleneck in agentic AI: systematic debugging and diagnostics at scale. By formalizing corpus-level trace diagnostics, Paper 2 provides tooling that can accelerate research and development across all LLM agent domains, giving it a broader and more foundational scientific impact.

Spreadsheet-RL addresses a highly practical and widely-applicable problem (spreadsheet automation) with a concrete RL-based methodology, new benchmark datasets, and clear quantitative improvements. Its combination of a scalable data collection pipeline, domain-specific benchmarks, and a realistic Excel environment provides substantial contributions with broad real-world impact. Paper 2 introduces an interesting abstraction for LLM agent skills but is more incremental in nature—primarily an engineering/architectural contribution with less rigorous evaluation (no clear quantitative baselines) and narrower scope of impact.

Paper 2 provides a comprehensive research ecosystem, including a novel RL framework, a new benchmark dataset, and an open gym environment. In AI research, such complete packages that enable further benchmarking and development typically drive higher citation rates and community adoption compared to the early-stage prototype and architectural proposals presented in Paper 1.

Paper 1 addresses a fundamental and timely question about how AI usage affects human skill development, with broad implications across education, workforce training, and AI policy. Its findings—that AI can either complement or substitute for human learning depending on informativeness—have wide applicability and relevance to ongoing societal debates about AI integration. Paper 2, while technically solid, is a more incremental engineering contribution focused on a specific application (spreadsheet automation via RL fine-tuning), with narrower impact scope and less conceptual novelty. Paper 1's insights are more likely to influence multiple fields and policy discussions.

Paper 1 likely has higher scientific impact due to stronger methodological innovation (RL fine-tuning in a realistic Excel environment), concrete artifacts (new benchmark dataset, gym environment, scalable data collection pipeline), and clear, quantifiable performance gains on standardized tasks. Its applications (spreadsheet automation) are broad across industries and align with timely interest in LLM agents and tool use, increasing cross-field adoption. Paper 2 is important for regulatory science infrastructure and FAIR data modernization, but appears more domain-specific and systems/standards-focused with less demonstrated empirical advancement, potentially limiting broader, faster uptake.

Spreadsheet-RL addresses a high-demand practical problem (AI-driven spreadsheet automation) with a novel RL-based framework, benchmark datasets, and a training environment. Its combination of real-world applicability (Excel automation), scalable data collection, and strong empirical results positions it for broad impact across AI, NLP, and productivity tool research. Paper 2 makes a solid theoretical contribution to assurance argument semantics using Subjective Logic, but targets a narrower safety/assurance community with limited empirical validation and fewer immediate practical applications.

Paper 2 is likely to have higher scientific impact due to its broadly applicable, timely modular specialization framework for LLMs under memory/latency constraints—an issue central to real-world deployment. Skillpacks + compression (SkillZip) can generalize across many domains, models, and agent settings, potentially influencing both systems and ML research. Paper 1 is strong and rigorous with clear applications, but its contributions are more domain-specific (Excel/spreadsheets) and may have narrower cross-field reach despite practical relevance.

Paper 2 addresses a more fundamental and broadly applicable problem—systematic diagnostics for LLM agents across any domain—while Paper 1 focuses on a specific application (spreadsheet automation). Paper 2's formalization of corpus-level trace diagnostics introduces a novel framework applicable to all LLM agent development, with strong empirical validation (30.4pp improvements). Its multi-agent diagnostic architecture has broader methodological impact across the rapidly growing LLM agent ecosystem, whereas Paper 1's contributions, while practical, are more incremental and domain-specific.

Paper 2 demonstrates a breakthrough in AI-assisted mathematics research by solving 9 open Erdős problems and proving 44 OEIS conjectures—genuinely advancing mathematical knowledge. This has profound implications across multiple mathematical subfields and establishes AI as a tool for resolving longstanding open problems. Its impact spans mathematics, formal verification, and AI research. Paper 1, while practically useful for spreadsheet automation, represents incremental engineering improvements on a narrower application domain with less fundamental scientific significance.