The Next Frontier in NLP: Smarter Agents, Not Just Bigger Models
Original blog link: CapeStart
I recently came across an interesting exploration of where NLP seems to be heading, especially around summarization systems. The piece argues that the real breakthrough isn’t just scaling models, but building smarter agent-like systems that collaborate—each part doing what it’s best at.
Rather than relying only on supervised learning or metrics like ROUGE, the post highlights how Reinforcement Learning from Human Feedback (RLHF) can actually help models produce summaries that humans prefer, not just summaries that look similar to reference text.
A hybrid architecture stood out:
A strong LLM acts as the “generator,”
A small open-source model learns how to craft prompts,
A reward model scores the outputs based on human preferences.
This creates a loop where the smaller model keeps improving at prompting the larger one, aiming for high-quality results without the high costs of training huge models directly.
The post also touches on challenges—like latency and how to assign credit during training—but it points toward a future where smarter, more interpretable agents take center stage over sheer model size.
If you’re interested in NLP, RLHF, or emerging summarization techniques, this perspective offers a thoughtful look at what might come next.