NonBioS 'Agentic' Evaluation of NVIDIA Nemotron

The Nemotron model from NVIDIA sparked our interest when it scored really well on the Arena Hard Benchmark - slotting it just below the frontier models from OpenAI and Claude. This was very impressive, given the fact that Nemotron is just a 70B parameter model and probably 5-10 times smaller than the frontier models. With an Arena Hard score of 70.9, it significantly outperformed its base model, Llama-3.1-70B-Instruct, which scored only 51.6. When we saw these numbers, we couldn't help but wonder if NVIDIA had achieved something remarkable with Nemotron.

Our initial excitement grew when we looked at other benchmarks. The model achieved an AlpacaEval 2 LC score of 57.6 and a GPT-4-Turbo MT-Bench score of 8.98. These weren't just good scores - they were approaching state-of-the-art territory. For context, these benchmarks are widely recognized as reliable indicators of model performance, and seeing a 70B model compete with much larger models made us sit up and take notice.

However, at NonBioS, we believe in looking beyond standard benchmarks. Our agentic evaluation framework specifically focuses on how models perform in real-world engineering scenarios. We're particularly interested in instruction following and problem-solving capabilities in complex technical contexts. This is where things got interesting.

When we put Nemotron through our evaluation pipeline, we noticed something fascinating. The model consistently produced more verbose outputs than the base Llama 70B, and it organized its responses in what we call a reasoning-response format. At first glance, this looked promising - after all, explicit reasoning chains are often valuable in technical problem-solving.

But as we dug deeper, we uncovered what I consider to be one of the most intriguing paradoxes in recent LLM development. Despite its enhanced reasoning capabilities, Nemotron doesn't show an increase in what we might call "fundamental intelligence." The behavior we observed reminded me of an interesting analogy - it's like asking someone with average problem-solving abilities to think more carefully before responding. While you get more structured and detailed responses, the underlying comprehension and decision-making capabilities remain unchanged. This became particularly evident when we presented the model with multi-faceted problems.

One of our most significant findings was how Nemotron handles what we call "question heads" - different aspects of a complex query that require varying levels of analysis. We found that the model often misallocated its attention, applying deep reasoning to simpler aspects while potentially overlooking more complex elements that genuinely needed that level of analysis. This cognitive inefficiency was particularly noticeable in engineering contexts where prioritization is crucial.

Our findings aligned with the Aider benchmarks that we have come to respect. At Aider's leaderboard, we saw Nemotron scoring 55% compared to the base Llama-3.1-70B-Instruct's 59%. This external validation helped confirm our observations about the trade-offs between structured reasoning and overall performance.

The bottom line from NonBioS is that Nemotron is a huge jump from Llama 3.1 70B when the task requires handling any type of logic where a clear chain of thought is essential. However, this comes at a cost of its longer instruction following capabilities or writing structured code.

From our perspective, Nemotron's case offers valuable insights for the future of LLM development. It shows us that enhancing specific capabilities like structured reasoning doesn't necessarily translate to improvements in general intelligence or overall performance. If you're currently using or considering Nemotron, our advice is to be strategic about its implementation. Use it where its strengths in detailed reasoning add value, but have alternatives ready for tasks requiring more straightforward responses. In our experience, this targeted approach to model selection is becoming increasingly crucial as the LLM landscape continues to evolve.