Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works weren't about a single breakthrough model. Instead, they challenged fundamental assumptions that academicians and corporations have quietly relied on: Bigger models mean better reasoning, RL creates new capabilities, attention is “solved” and generative models inevitably memorize.

This year’s top papers collectively point to a deeper shift: AI progress is now constrained less by raw model capacity and more by architecture, training dynamics and evaluation strategy.

Below is a technical deep dive into five of the most influential NeurIPS 2025 papers — and what they mean for anyone building real-world AI systems.

1. LLMs are converging—and we finally have a way to measure it

Paper: Artificial Hivemind: The Open-Ended Homogeneity of Language Models

For years, LLM evaluation has focused on correctness. But in open-ended or ambiguous tasks like brainstorming, ideation or creative synthesis, there often is no single correct answer. The risk instead is homogeneity: Models producing the same “safe,” high-probability responses.

This paper introduces Infinity-Chat, a benchmark designed explicitly to measure diversity and pluralism in open-ended generation. Rather than scoring answers as right or wrong, it measures:

• Intra-model collapse: How often the same model repeats itself

• Inter-model homogeneity: How similar different models’ outputs are

The result is uncomfortable but important: Across architectures and providers, models increasingly converge on similar outputs — even when multiple valid answers exist.

Why this matters in practice

For corporations, this reframes “alignment” as a trade-off. Preference tuning and safety constraints can quietly reduce diversity, leading to assistants that feel too safe, predictable or biased toward dominant viewpoints.

Takeaway: If your product relies on creative or exploratory outputs, diversity metrics need to be first-class citizens. 

2. Attention isn’t finished — a simple gate changes everything

Paper: Gated Attention for Large Language Models

Transformer attention has been treated as settled engineering. This paper proves it isn’t.

The authors introduce a small architectural change: Apply a query-dependent sigmoid gate after scaled dot-product attention, per attention head. That’s it. No exotic kernels, no massive overhead.

Across dozens of large-scale training runs — including dense and mixture-of-experts (MoE) models trained on trillions of tokens — this gated variant:

• Improved stability

• Reduced “attention sinks”

• Enhanced long-context performance

• Consistently outperformed vanilla attention

Why it works

The gate introduces:

• Non-linearity in attention outputs

• Implicit sparsity, suppressing pathological activations

This challenges the assumption that attention failures are purely data or optimization problems.

Takeaway: Some of the biggest LLM reliability issues may be architectural — not algorithmic — and solvable with surprisingly small changes.

3. RL can scale — if you scale in depth, not just data

Paper: 1,000-Layer Networks for Self-Supervised Reinforcement Learning

Conventional wisdom says RL doesn’t scale well without dense rewards or demonstrations. This paper reveals that that assumption is incomplete.

By scaling network depth aggressively from typical 2 to 5 layers to nearly 1,000 layers, the authors demonstrate dramatic gains in self-supervised, goal-conditioned RL, with performance improvements ranging from 2X to 50X.

The key isn’t brute force. It’s pairing depth with contrastive objectives, stable optimization regimes and goal-conditioned representations

Why this matters beyond robotics

For agentic systems and autonomous workflows, this suggests that representation depth — not just data or reward shaping — may be a critical lever for generalization and exploration.

Takeaway: RL’s scaling limits may be architectural, not fundamental.

4. Why diffusion models generalize instead of memorizing

Paper: Why Diffusion Models Don't Memorize: The Role of Implicit Dynamical Regularization in Training

Diffusion models are massively overparameterized, yet they often generalize remarkably well. This paper explains why.

The authors identify two distinct training timescales:

• One where generative quality rapidly improves

• Another — much slower — where memorization emerges

Crucially, the memorization timescale grows linearly with dataset size, creating a widening window where models improve without overfitting.

Practical implications

This reframes early stopping and dataset scaling strategies. Memorization isn’t inevitable — it’s predictable and delayed.

Takeaway: For diffusion training, dataset size doesn’t just improve quality — it actively delays overfitting.

5. RL improves reasoning performance, not reasoning capacity

Paper: Does Reinforcement Learning Really Incentivize Reasoning in LLMs?

Perhaps the most strategically important result of NeurIPS 2025 is also the most sobering.

This paper rigorously tests whether reinforcement learning with verifiable rewards (RLVR) actually creates new reasoning abilities in LLMs — or simply reshapes existing ones.

Their conclusion: RLVR primarily improves sampling efficiency, not reasoning capacity. At large sample sizes, the base model often already contains the correct reasoning trajectories.

What this means for LLM training pipelines

RL is better understood as:

• A distribution-shaping mechanism

• Not a generator of fundamentally new capabilities

Takeaway: To truly expand reasoning capacity, RL likely needs to be paired with mechanisms like teacher distillation or architectural changes — not used in isolation.

The bigger picture: AI progress is becoming systems-limited

Taken together, these papers point to a common theme:

The bottleneck in modern AI is no longer raw model size — it’s system design.

• Diversity collapse requires new evaluation metrics

• Attention failures require architectural fixes

• RL scaling depends on depth and representation

• Memorization depends on training dynamics, not parameter count

• Reasoning gains depend on how distributions are shaped, not just optimized

For builders, the message is clear: Competitive advantage is shifting from “who has the biggest model” to “who understands the system.”

Maitreyi Chatterjee is a software engineer.

Devansh Agarwal currently works as an ML engineer at FAANG.