The AI Research Dilemma: Polish vs Foundation

Imagine you're working to build AI that meaningfully changes lives—helping students learn, assisting developers with code, or enabling everyday people to navigate a complex world. Today's AI research landscape offers impressive benchmark results and polished user interfaces, but often at the expense of fundamental architectural improvements. A recent paper, Outperforming DeepSeekR1-32B with OpenThinker2 (April 3, 2025), provides an illuminating case study of this phenomenon. Let's examine what this reveals about our current priorities and consider how we might chart a more productive path forward.

OpenThinker2: A Study in Optimization Over Architecture

The OpenThinker2 team recently introduced two models—32B and 7B parameter versions—achieving notable scores: 76.7 on AIME24 and 90.8 on MATH500, surpassing the DeepSeek-R1-Distill-32B. Their methodology? Creating OpenThoughts2-1M, a meticulously curated dataset of 1 million examples developed through 26 distinct strategies, distilled via DeepSeek-R1, and refined with their Evalchemy framework applied five separate times. Notably, they employed supervised fine-tuning (SFT) rather than reinforcement learning (RL) approaches and have released the models as open-source.

This approach represents a common pattern in AI research today—extensive post-hoc optimization on top of existing foundation models rather than fundamental improvements to the pretraining methodology itself. From OpenAI's RLHF to various test-time compute optimizations, much of our field has become focused on enhancement techniques rather than architectural innovations. Let's analyze the limitations of this approach and what opportunities we might be missing.

Limitation #1: Post-training Refinement Over Foundational Capability

The OpenThinker2 team dedicated substantial resources to developing their 1M-row dataset to enhance the Qwen2.5-Instruct foundation. But this raises a key question: why not direct those resources toward improving the base model architecture first? Pretraining establishes the fundamental capabilities—get this right, and the need for extensive compensatory fine-tuning diminishes significantly.

The performance gap between variants tells an important story: OpenThinker2-7B achieved just 33.3 on AIME25 compared to 57.3 for R1-Distill-7B. This suggests that no amount of optimization can fully compensate for limitations in the underlying model architecture. Yet the field often treats fine-tuning as the primary innovation vector, when architectural improvements to unsupervised learning or RL-informed pretraining approaches could yield more substantial gains.

Core Issue: Researchers face incentives to maximize benchmark performance through optimization rather than tackle the more challenging problem of foundational improvements. Funding structures and academic prestige reward quick, measurable gains over longer-term architectural innovations. But who benefits when we optimize for narrow academic benchmarks rather than building more capable foundation models?

Path Forward: We should consider organizational structures that separate pretraining architecture research from fine-tuning optimization work. Investing in fundamental innovation would create a stronger base for all downstream applications.

Limitation #2: Benchmark Optimization Over Real-world Utility

RLHF techniques gave us conversational AI capabilities that benefited broad user populations. In contrast, OpenThinker2 appears optimized specifically for mathematical benchmarks like AIME24, with less evident consideration for real-world application scenarios. This highlights a concerning trend toward ungrounded metrics—impressive scores that don't necessarily translate to practical value.

The substantial effort behind the 1M-row dataset could alternatively have focused on developing an AI tutor measurable by actual student learning outcomes rather than abstract mathematical benchmark points. Instead, the work appears primarily aimed at advancing within the research community rather than solving real-world problems.

Core Issue: We've created a feedback loop where benchmarks have become ends unto themselves rather than proxies for real-world capability. Market dynamics reward the appearance of progress through metrics, even when those metrics don't align with practical utility. Why optimize for test scenarios when users need solutions to real problems?

Path Forward: We should ground our evaluations in authentic use cases—measuring educational AI by learning outcomes, coding assistants by developer productivity, and so on. Evaluation experts should design metrics that reflect practical value, not just academic achievement.

Limitation #3: Complexity Without Specialization

The OpenThinker2 approach—26 strategies, 1M examples, 5 iterations—reflects another common pattern: single research teams attempting to excel across all dimensions of AI development simultaneously. This makes it difficult to develop deep expertise in any single area. RLHF development shows similar patterns, with teams handling everything from data collection to model architecture to deployment. A more specialized approach might separate pretraining architectural research, application-specific fine-tuning, and user experience design into distinct but coordinated workstreams.

Core Issue: Funding models and publication incentives push research teams toward comprehensive but potentially shallow contributions across multiple domains rather than deep expertise in complementary specialties. No team can maintain equal focus on architecture, optimization, and application when resources are limited. Why pursue breadth when depth might yield more transformative advances?

Path Forward: Building more diverse, specialized teams would allow focused expertise—some researchers could concentrate on architecture improvements while others develop application-specific refinements. This division of labor would need to explicitly balance market-driven priorities with technical depth.

The Optimization Trap: Diminishing Returns

The AI research community's fixation on benchmarks as the ultimate goal and fine-tuning as the primary methodology represents a form of optimization trap. OpenThinker2's labor-intensive dataset curation, RLHF's feedback collection, and various test-time computation tricks may yield increasingly marginal improvements while fundamental architectural innovation remains underexplored. The resources dedicated to optimization often dwarf those allocated to architectural innovation, yet the latter typically offers more sustainable long-term progress.

Missing Consideration: Sustainability. Creating bespoke, million-example datasets for each task or endlessly iterating optimization techniques doesn't scale efficiently. Would it not be more productive to improve the fundamental capabilities once rather than repeatedly compensating for limitations through post-hoc refinement?

Constructive Path Forward

This analysis isn't merely academic—it's intended as a catalyst for reconsideration. The OpenThinker2 paper serves as a mirror reflecting our field's current priorities: optimization over architecture, benchmarks over utility, breadth over depth. The alternatives—architecture-first innovation, application-grounded evaluation, specialized expertise—offer clearer paths to meaningful progress.

Our current reality presents a challenge: a field incentivized toward quick wins through optimization, influenced by publication and funding pressures, often neglecting the foundational work that could free us from this cycle of diminishing returns.

Let's recalibrate—invest in architectural innovation, measure success through real-world impact, and develop specialized expertise that advances the field systematically. True progress requires not just polishing what exists but reimagining what's possible at the foundational level.