NeurIPS 2025’s uncomfortable RL lesson: depth beats “more data” (plus gated attention, diffusion anti-memorization, and the rise of the AI hivemind)

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NeurIPS papers have a funny habit: they don’t just propose a new tweak, they quietly invalidate your last six months of engineering decisions. And while the 2025 conference (held in early December 2025 in San Diego) produced its usual flood of cleverness, a handful of works did something more interesting: they attacked the comfortable assumptions that many teams have been using as architectural security blankets.

This article is based on (and links back to) the original VentureBeat guest post, “Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)” by Maitreyi Chatterjee and Devansh Agarwal, published on January 17, 2026. citeturn1view0

But rather than rehashing their five takeaways, we’ll expand them into what enterprise builders actually need: what changed, why it matters, where the traps are, and how to operationalize these ideas if you’re shipping models instead of just admiring them.

NeurIPS 2025 in one sentence: AI is now systems-limited

The VentureBeat piece frames the shift neatly: progress is becoming constrained less by raw model size and more by architecture choices, training dynamics, and evaluation strategy. citeturn1view0 That’s not just a philosophical statement. It’s a practical warning that “scale” isn’t a monolith. You can scale parameters and still hit a wall if you don’t scale the right mechanism.

In 2023–2024, the default answer to many model problems was essentially: “more data, more compute, more parameters.” By 2025, the best papers were increasingly saying: “Sure, but which failure mode are you actually buying down?” If your bottleneck is representation collapse, attention sinks, policy entropy collapse, or evaluation that rewards the wrong behavior, then brute-force scaling can be an expensive way to learn nothing.

With that, let’s dig into the five themes: (1) LLM homogeneity and how to measure it, (2) gated attention and attention sinks, (3) RL scaling via depth, (4) diffusion models and why they often don’t memorize (until they do), and (5) what RL with verifiable rewards is really doing to “reasoning.”

1) The “Artificial Hivemind” problem: LLMs converging on the same safe answers

What the paper claims

The NeurIPS 2025 paper “Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)” argues that LLMs increasingly produce homogeneous outputs on open-ended prompts, even across different providers and architectures. It introduces Infinity-Chat, a dataset of roughly 26K diverse open-ended user queries, mined from real-world chatbot interactions, and proposes ways to measure both intra-model repetition and inter-model homogeneity. citeturn2view0

This matters because most evaluation frameworks still treat language as if there is one correct answer and everything else is “noise.” That’s fine for math verification. It’s disastrous for ideation products, writing assistants, strategic planning tools, and any workflow where value comes from exploring a space of plausible options rather than landing on a single truth.

Why “more alignment” can quietly reduce diversity

Enterprise buyers often ask for “safer” and “more aligned” outputs. Reasonable request. But the uncomfortable side effect—highlighted in the VentureBeat analysis—is that preference tuning and safety constraints can compress output distributions toward the same bland, high-probability responses. citeturn1view0

That creates a product experience that feels like:

  • Predictability masquerading as reliability (“it always answers politely!”) while ideas get less interesting.
  • Consensus bias, where a model defaults to the dominant viewpoint even when alternative viewpoints are equally valid.
  • “Model monoculture” risk, where many organizations end up implementing the same assistant persona with the same blind spots.

What to do with Infinity-Chat as a builder

The key contribution here isn’t that models sometimes repeat themselves—we all knew that by reading five consecutive “here are 10 tips” listicles. The contribution is that Infinity-Chat tries to make diversity measurable at scale. citeturn2view0

Three practical moves for teams:

  • Add diversity to evaluation gates. If you only track correctness and toxicity, you’ll optimize into a creative coma. Consider scoring open-ended tasks for semantic variety across samples and across model variants.
  • Separate “safety” from “style.” Many stacks treat safety tuning as a single knob, but user experience often needs safe and varied. Structure prompts, decoding, and post-processing so safety doesn’t automatically imply sameness.
  • Measure inter-model similarity when choosing vendors. If two model families converge on the same outputs, “multi-model redundancy” may not buy you diversity—just more invoices.

2) Attention isn’t done: gated attention and the war on attention sinks

The paper: a tiny gate with big effects

The NeurIPS 2025 paper “Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free” (from researchers including the Qwen team at Alibaba) proposes an almost offensively small architectural change: apply a query-dependent sigmoid gate after scaled dot-product attention, per attention head. citeturn3view0

They report broad improvements across many training runs, including dense and mixture-of-experts configurations. The authors attribute the gains to two main effects: (1) introducing non-linearity into what can otherwise behave like a low-rank linear mapping, and (2) inducing sparsity that helps suppress pathological activations. citeturn3view0

Attention sinks: why your long context collapses onto junk tokens

The paper also highlights attention sinks, where a model disproportionately attends to certain tokens (often the beginning-of-sequence token or low-information tokens), which harms effective context usage. The authors report their gating mechanism mitigates attention sink behavior and improves long-context generalization. citeturn3view0

Why should anyone outside “Transformer Studies Department” care? Because attention sinks are an engineer’s version of a ghost in the machine: you give the model 128K context, and it spends 40% of its attention budget staring lovingly at the first token like it’s going to reveal the secrets of the universe.

Enterprise implications: reliability fixes can be architectural, not just data-driven

Many orgs treat LLM reliability as a data curation and prompt engineering problem. Those matter. But gated attention is a reminder that some failure modes are simply architectural. If a model’s attention mechanism is structurally prone to sinks, you can’t prompt your way out forever.

What this changes operationally:

  • Architecture experimentation belongs in applied teams. You don’t need to publish to benefit; even “minor” attention variants can materially improve stability.
  • Long-context benchmarks should include sink diagnostics. If you evaluate only on QA accuracy, you may miss that the model is “cheating” via shallow heuristics.
  • MoE + gating is a systems story. The paper’s results include MoE setups trained at very large token counts. This reinforces that the best gains increasingly come from combining architectural choices with scaling strategy, not from any single trick. citeturn3view0

3) Reinforcement learning can scale—if you scale representation depth

The NeurIPS Best Paper thread that RL teams will argue about for a year

The most headline-grabbing RL result in the VentureBeat roundup comes from “1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities”. The authors (Kevin Wang, Ishaan Javali, Michał Bortkiewicz, Tomasz Trzciński, Benjamin Eysenbach) argue that RL’s scaling limits are not purely fundamental—depth is a key unlock. citeturn4view0

They work in an unsupervised goal-conditioned setting (no demonstrations, no rewards). The agent explores from scratch and learns to reach commanded goals, evaluated on simulated locomotion and manipulation. They report improvements ranging from doubling performance to as much as 50× on certain humanoid tasks, with performance sometimes jumping at “critical depths” rather than scaling smoothly. citeturn4view0

This paper was also featured on the NeurIPS blog in the context of awards coverage. citeturn0search3

Why depth is not the same as “more parameters”

In practice, teams often scale RL by widening networks or increasing replay, environment steps, and reward shaping. Depth is avoided because deep RL is famously unstable: gradient interference, non-stationarity, brittle optimization, and the dreaded “it learned, then it forgot everything.”

The paper’s argument is that with the right building blocks—self-supervision (contrastive objectives), stable optimization regimes, and appropriate scaling of batch size—depth becomes usable and can fundamentally change what policies emerge. citeturn4view0

If you’re thinking “this sounds like the deep learning story from 2015, just re-enacted in RL cosplay,” you’re not wrong—and that’s kind of the point. RL may have been stuck in a shallow-network rut while other fields happily stacked hundreds of layers.

How contrastive RL fits in

The 1000-layer work builds on contrastive reinforcement learning, a line of work that treats representation learning not as an auxiliary objective but as a core RL mechanism. The earlier paper “Contrastive Learning as Goal-Conditioned Reinforcement Learning” by Eysenbach et al. describes how contrastive representations can correspond to a goal-conditioned value function and can outperform non-contrastive baselines across goal-conditioned tasks. citeturn6academia12

What “representation depth” means for agentic systems (not just robotics)

The VentureBeat post makes the leap from robotics to agentic workflows: if representation depth enables better generalization and exploration, then it’s relevant to autonomous systems beyond physical control—think scheduling agents, tool-using code agents, or systems that must learn robust strategies from sparse success signals. citeturn1view0

There’s a practical translation here for enterprise AI teams:

  • Stop assuming RL is “just for fine-tuning” by definition. The conventional wisdom (self-supervised pretrain, RL finetune) is useful, but it can become dogma. The paper explicitly challenges the idea that RL feedback is too information-poor to train deep networks. citeturn0search3
  • Architectures are part of RL scaling. If your RL stack is still using 2–5 layer MLPs because “that’s what works,” you may be living in a performance ceiling you accidentally inherited.
  • Depth will demand systems investment. Deep networks increase training cost, but the larger pain is stability engineering: normalization, residual design, batch sizing, gradient handling, and instrumentation.

Related signals: scaling RL via optimization and sparsity

Even outside the 1000-layer result, 2025 saw multiple efforts attacking RL scaling pathologies directly. For example, “Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning” studies how non-stationarity combined with gradient pathologies and architectural choices contributes to scaling failures, proposing interventions to stabilize gradient flow. citeturn0academia13

Another 2025 arXiv work, “Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning,” argues that introducing static sparsity (via one-shot random pruning) can improve parameter efficiency and resistance to issues like plasticity loss and gradient interference. citeturn0academia12

These aren’t the same thesis as “go 1024 layers deep,” but they rhyme: RL scaling is constrained by training dynamics and architecture pathologies, not merely by data scarcity.

4) Diffusion models: why they often don’t memorize (until training runs long enough)

The paper: memorization is delayed by dynamics, not magically absent

Diffusion models have a reputation for “generalizing well” despite being huge. The NeurIPS 2025 paper “Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training” investigates this mystery and proposes a more nuanced answer: memorization emerges on a different timescale than useful generation quality. citeturn5academia12

The authors identify two timescales: an early time where generative quality improves (τgen) and a later time when memorization emerges (τmem). Critically, τmem increases linearly with dataset size, while τgen remains roughly constant, creating a growing window where models improve without memorizing—unless you keep training past it. citeturn5academia12

The paper was accepted at NeurIPS 2025 and appears in the NeurIPS virtual poster listing, including presentation details. citeturn5search2

Why this matters: compliance, data governance, and “creative” claims

For enterprise users, “memorization” isn’t an abstract generalization question; it’s a governance problem. If your generative model can reproduce training data too closely, you face:

  • IP risk (output resembling copyrighted or proprietary material)
  • privacy risk (leakage of sensitive examples)
  • regulatory exposure depending on data provenance and jurisdiction

The diffusion finding suggests that “it doesn’t memorize” is not a permanent property. It can be a training-regime property. Dataset size and training time interact in a predictable way, implying you can engineer for a safer window—but you can also accidentally train your way out of it. citeturn5academia12

Operational takeaway: training budgets are now a safety parameter

This is the part many teams miss: training time is usually treated as a cost decision (“how much compute can we afford?”). This paper argues training time is also a behavioral parameter: it can move you from generalization into memorization. citeturn5academia12

Practical recommendations:

  • Instrument memorization tests during training. Don’t just evaluate FID or human preference; run nearest-neighbor and duplication checks across checkpoints.
  • Use dataset scaling as a governance control. Larger datasets not only improve variety; they can delay memorization effects according to the paper’s analysis.
  • Document stopping criteria. “We stopped at X steps” should be justified by more than “the cluster reservation ended.”

5) RL with verifiable rewards: boosting performance or expanding reasoning capacity?

The NeurIPS takeaway: RL reshapes distributions

The VentureBeat article’s fifth point is the most strategically spicy: RL improves reasoning performance, not reasoning capacity. citeturn1view0 The cited paper, “Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?”, evaluates RL with verifiable rewards (RLVR) using large-sample pass@k tests, and argues RLVR often increases pass@1 by biasing the model toward rewarded trajectories but may not create fundamentally new reasoning patterns. citeturn4view1

The authors report that at large k, base models can match or even exceed RLVR-tuned models on pass@k, implying that RLVR may be primarily improving sampling efficiency—bringing already-present correct reasoning paths to the front of the distribution. citeturn4view1

Important context: there is active disagreement on how to measure this

In 2025, the community started arguing not just about whether RLVR “adds reasoning,” but whether the metrics used to claim it does (or doesn’t) are valid.

For example, the 2025 paper “Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs” argues that pass@k can be misleading because it may credit correct final answers that arise from incorrect or incomplete chains of thought, proposing a refined metric (CoT-Pass@k) and presenting evidence that RLVR can generalize correct reasoning under that lens. citeturn6academia13

Translation: the “RLVR doesn’t add capacity” conclusion is not a settled law of physics. It is a claim conditioned on evaluation choices—and the evaluation choices are themselves a live research topic.

What this means for enterprise LLM training pipelines

Even if you accept the skeptical view, the practical guidance is still extremely useful:

  • Treat RLVR as distribution shaping. It can make your model more likely to emit high-reward completions quickly, which matters for latency and cost. citeturn4view1
  • Watch for diversity collapse. If RLVR sharpens the policy too hard, you may gain pass@1 but lose breadth under sampling—or lose creativity in open-ended tasks. citeturn4view1
  • Use multi-metric evaluation. Track pass@1, pass@k, reasoning consistency, and diversity metrics (especially if your product is not purely “one correct answer”).
  • Consider pairing RL with distillation or architectural changes. The skeptical paper explicitly notes distillation can introduce new knowledge in ways RLVR may not. citeturn4view1

Putting it together: a playbook for builders in 2026

These papers rhyme with each other in a way that should make platform teams slightly nervous and slightly excited.

Here’s the unifying message: modern AI performance is increasingly gated by mechanisms—how representations form, how attention behaves, how training dynamics unfold, and how evaluation signals shape what you think you built.

Checklist: questions to ask your stack this quarter

  • Are we measuring the right thing? If your model is used for ideation or decision support, do you track diversity/homogeneity in outputs (Infinity-Chat-style), or only correctness and safety?
  • Do we understand our context failure mode? If long-context performance is unreliable, have you diagnosed attention sinks and tested architectural mitigations like gating?
  • Are we stuck in shallow RL by habit? If you’re using RL for agents, have you tested whether deeper representations (plus stabilizing tricks) change what behaviors emerge?
  • Is training time a governance decision? For generative models (especially diffusion), do you treat “train longer” as an automatic good—or as something that might push you toward memorization regimes?
  • Do our RLVR gains come from capacity or sampling? And does that distinction matter for our product, latency, and cost model?

One mildly funny but serious prediction

In 2026, the competitive advantage won’t just be “who trained the biggest model.” It’ll be “who built the least self-deceiving evaluation harness.” Infinity-Chat exists because we were grading creativity with correctness metrics. RLVR debates exist because we were grading reasoning capacity with success-under-guessing metrics. Attention gating papers exist because we treated attention as solved while production context windows grew faster than our understanding of failure modes.

Or, to put it in enterprise terms: your next big performance gain may come from a benchmark, not a bigger GPU bill.

Sources

Bas Dorland, Technology Journalist & Founder of dorland.org