Latent Contextual Reinforcement: Teaching Language Models to Think Better Without Changing Their…

Latent Contextual Reinforcement: Teaching Language Models to Think Better Without Changing Their Weights Adeel Ahmad I trained a 4-billion-parameter language model on a laptop with 8 gigabytes of RAM. It took a few hours and produced an adapter file smaller than most photographs. Every standard evaluation metric - cosine similarity, CKA, benchmark scores, perplexity - says the model did not change. It did. The model’s behaviour transformed: it reasons efficiently, follows structured thinking patterns it never exhibited before, and adopted an entirely new identity.