Researchers have introduced PRIME (Process Reinforcement through Implicit Rewards), an open-source solution aimed at enhancing the reasoning abilities of language models beyond traditional imitation or distillation techniques. PRIME integrates implicit process reward modeling with reinforcement learning, allowing for online updates using samples generated by policy models. This approach significantly improves model performance while reducing data and computational resource requirements. In a related advancement, a 7 billion parameter model, Eurus-2-7B-PRIME, was trained using PRIME, demonstrating superior mathematical capabilities compared to larger models such as GPT-4o and Llama-3.1-70B, without relying on distillation or imitation learning. Additionally, researchers from KAIST and DeepAuto have developed CoLoR (Compression for Long Context Language Model Retrieval), which enhances retrieval tasks by making them 1.91 times faster while improving performance.