Researchers led by Damani et al. introduced a method to train language models to express their uncertainty by adding a calibration reward to reinforcement learning from verifiable rewards (RLVR). The …
Researchers have developed Guidance Contrastive Policy Optimization (GCPO), a new algorithm that assigns per-token credit by contrasting model predictions under positive and negative prompts, addressi…
The Granite 4.1 family consists of dense, decoder-only LLMs (3B, 8B, and 30B parameters) trained from scratch on approximately 15 trillion tokens through a five-phase pre-training pipeline that progre…
FIPO (Future-Impact-based Policy Optimization) is a reinforcement learning method that improves LLM reasoning by assigning token-level credit based on each token's future impact on the policy, rather …
Ecom-RLVE, a framework that extends the RLVE system from single-turn reasoning puzzles to multi-turn, tool-augmented e-commerce conversations. It provides eight verifiable environments—such as product…