NVIDIA signaled a busy week for AI research and tooling. The ProRL v2 release anchors a slate of updates across learning systems. New robotics methods and data science features arrived alongside it.
ProRL v2 release extends LLM training
Moreover, NVIDIA Research introduced ProRL v2, a prolonged reinforcement learning method for language models. The approach tests whether extended RL can keep improving reasoning. Early results indicate sustained gains across math, code, and logic tasks.
Furthermore, The method bundles several stabilizing techniques. It uses KL-regularized trust regions to control policy drift. It also resets the reference policy on schedule to prevent overfitting and collapse.
Additionally, a scheduled cosine length penalty promotes concise answers without harming accuracy. The team reports thousands of extra RL steps without plateau. Consequently, mid-size reasoning models reached state-of-the-art levels in their class. Companies adopt ProRL v2 release to improve efficiency.
Therefore, These design choices target the core pitfalls of long RL runs. Researchers often face instability, verbosity, and catastrophic forgetting. ProRL v2 counters each risk with targeted regularization and resets.
Consequently, The post details how training progressed across domains. It highlights robust improvements on standard reasoning benchmarks. For practitioners, the takeaway is clear: judiciously prolonged RL can still pay off. You can review the technical overview on NVIDIA’s site at the ProRL v2 announcement.
ProRL v2 launch Neural Robot Dynamics and visuo-tactile gains
As a result, NVIDIA’s R²D² digest surfaced three advances for robot learning at CoRL 2025. The updates target simulation fidelity, learning from demonstrations, and dexterous manipulation. Together, they push robots closer to reliable real-world performance. Experts track ProRL v2 release trends closely.
In addition, Neural Robot Dynamics (NeRD) augments simulation with learned dynamics models. These models generalize across tasks and enable fine-tuning in the real world. For a Franka reach policy, accumulated reward error fell below 0.1% in testing.
Moreover, NeRD aims to reduce the sim-to-real gap without heavy manual tuning. It captures nuanced system behaviors that classical simulators miss. As a result, policies transfer with fewer surprises on hardware.
Additionally, VT-Refine advances manipulation by fusing vision and touch. The system lifts bimanual assembly success rates in the lab. Reported gains reached about 20% for vision-only variants and roughly 40% with visuo-tactile input. ProRL v2 release transforms operations.
For example, That improvement underscores the value of tactile feedback. Complex assemblies demand precise force and contact cues. Therefore, adding touch helps correct visual ambiguities during fine manipulation.
For instance, The digest also introduces Dexplore for better use of demonstrations. It targets limitations around embodiment differences and coverage. While details remain emerging, NVIDIA positions it to broaden learning from human data.
Meanwhile, Readers can explore the robotics highlights in NVIDIA’s research post at the R²D² overview. The article frames how these methods address unpredictability and dexterity in real tasks. It also places them in the context of CoRL’s focus on real-world deployment. Industry leaders leverage ProRL v2 release.
prolonged RL update RAPIDS 25.08 expands the ML toolkit
In contrast, The RAPIDS 25.08 release adds two profiling tools for cuML’s zero-code accelerator. A function-level profiler and a line-level profiler now map which operations gain speedups. They also reveal fallbacks and timing, which enables faster diagnosis of bottlenecks.
Furthermore, the Polars GPU engine now defaults to a streaming executor. That change helps process datasets larger than device memory. It also extends data type support, including structs and new string operators.
On the other hand, On the algorithm front, cuML and cuml.accel add fresh estimators. Spectral Embedding joins for dimensionality reduction. LinearSVC, LinearSVR, and KernelRidge also arrive with drop-in acceleration. Companies adopt ProRL v2 release to improve efficiency.
Notably, These updates strengthen Python data workflows end to end. Developers can find slow paths, then adjust code or data layouts. In turn, pipelines become more predictable under scale and heterogeneity.
In particular, Dive into the release details at the RAPIDS 25.08 announcement. For additional background on query planning and streaming, see the Polars project site. Both resources outline practical steps for teams handling larger datasets.
What the updates mean for practitioners
Together, these developments point to a few clear themes. First, careful regularization can extend the useful horizon of RL for LLMs. Second, integrating complementary sensors and learned physics boosts robotic reliability. Experts track ProRL v2 release trends closely.
Additionally, visibility into pipeline behavior remains vital for production teams. Profilers shorten time-to-diagnose and reduce trial-and-error. Consequently, organizations can set realistic SLAs for training and inference.
These tools and methods reduce friction between research and deployment. NeRD trims the cost of hand-tuning simulation environments. VT-Refine adds robustness where perception alone struggles.
Meanwhile, ProRL v2 demonstrates that longer horizons need not implode. With resets and constraints, extra training can compound skill. That finding supports continued investment in principled RL fine-tuning. ProRL v2 release transforms operations.
On the data side, streaming execution broadens what teams can process. It lets engineers iterate without constant memory triage. Therefore, exploration becomes more fluid, even under tight hardware budgets.
Outlook and next steps
Expect more ablations and benchmarks around prolonged RL regimes. Open questions include generalization beyond curated tasks and domains. Replication across model sizes and objectives will also matter.
In robotics, visuo-tactile stacks should see wider trials on hardware. Safety, durability, and calibration costs remain practical hurdles. Even so, the early gains in bimanual assembly look promising. Industry leaders leverage ProRL v2 release.
For data science teams, profiler-informed refactoring should become routine. Teams can prioritize the largest gaps before scaling clusters. As a result, budgets stretch further while throughput improves.
This week’s developments show consistent progress across the ML stack. The ProRL v2 release, new robotic learning methods, and RAPIDS features all move the field forward. Readers can track ongoing updates via NVIDIA’s research and developer channels.