In the race to make Large Language Models (LLMs) reason like humans—or better—most researchers obsess over one thing: prompting. Chain-of-thoughts, few-shot demos, scratchpads, tools. But a new study from NVIDIA suggests something even more fundamental: it’s not just how you prompt them—it’s how long you train them.
Their paper, Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training, explores how stretching reinforcement learning (RL) over time unlocks broader, more stable, and more versatile reasoning in LLMs. This isn’t just about incremental gains—it’s about escaping reasoning ruts.
🧠 From Reward Hacks to Real Reasoning
Standard RLHF (Reinforcement Learning with Human Feedback) has a known Achilles’ heel: models learn to exploit reward models, often producing polite but shallow answers. NVIDIA sidesteps this by using verifiable rewards—where task success can be programmatically judged. Think: solving a math equation or passing test cases in code. It’s automatic, unambiguous, and free from the fluff.
🧪 The Long-Haul Strategy: Key Ingredients
The team used a compact 1.5B parameter model (Nemotron-Research-Reasoning-Qwen) and trained it across five reasoning-rich domains:
| Domain | Data Size | Reward Type | Dataset Source |
|---|---|---|---|
| Math | 40K | Binary | DeepScaleR |
| Code | 24K | Continuous | Eurus-2-RL |
| STEM QA | 25K | Binary | SCP-116K |
| Logic Puzzles | 37K | Continuous | Reasoning Gym |
| Instruction Following | 10K | Continuous | Llama-Nemotron |
Then they stacked several innovations:
- Group Relative Policy Optimization (GRPO) – a critic-free variant of PPO that uses group-based advantage normalization.
- DAPO Enhancements – Decoupled clipping and dynamic prompt sampling, promoting diverse and stable learning.
- KL Regularization with Reset – Penalizing policy drift with KL loss, but periodically resetting the reference model to prevent stagnation.
These tweaks weren’t just technical niceties—they addressed serious RL pitfalls like entropy collapse, exploration-exploitation imbalance, and reward saturation.
📈 The Payoff: Reasoning That Generalizes
The result? Compared to its own distilled base model, Nemotron-Research-Reasoning-Qwen achieved:
- +14.7% on math benchmarks
- +13.9% on code generation
- +54.8% on logic puzzles
- +25.1% on STEM reasoning
- +18.1% on instruction-following
Crucially, this general-purpose model performed on par with math- and code-specialized models, without sacrificing one domain for another. That’s a big deal for enterprise use, where agent versatility is more valuable than benchmark heroics.
🧭 Lessons for AI Agents and Automation
For Cognaptus readers building business agents, the implications are clear:
- Reasoning robustness comes from prolonged, multi-domain RL—not just data scale.
- Entropy preservation (via dynamic sampling and decoupled clipping) is vital for exploration and long-run gains.
- Resetting the reference policy may sound like a hack, but it’s more like refreshing a compass—it lets the agent reorient when it hits a plateau.
Imagine applying these ideas to agents that write reports, navigate ambiguous customer instructions, or make multi-step API calls. Rather than relying on brittle prompting tricks, you’d be cultivating reasoning habits over time—habits that are measurable, correctable, and aligned to verifiable success.
🔮 Final Thought
The paper is a reminder: depth of reasoning isn’t just a function of model size—it’s a product of training philosophy. Long-horizon RL, when grounded in verifiable signals and stabilized with smart interventions, may be the key to generalist models that don’t just follow instructions, but figure them out.
Cognaptus: Automate the Present, Incubate the Future.