Latent context compression replaces a document with a short sequence of learned vectors, but whether this beats simply keeping raw tokens under the same decoder-context budget is rarely tested. We run that test on held-out document QA, matching budgets per example against raw-token truncation and selector-specific readers trained on first-N, LLMLingua-2, and question-conditioned BM25 contexts. Our minimal compressor consists of a text encoder, learned pooling, and direct projection. It beats every tested common-cap retention baseline at nominal pooling factors cr=8 and cr=16, reaching answer perplexity 3.464 versus 5.349 for the strongest baseline at cr=8. The advantage persists in first-sentence free generation, and a manual check of answers is directionally consistent. Adding a post-pooling transformer hurts with twelve layers and with one layer. The twelve-layer result holds in the discovery run, two replications, and a second decoder family. Removing the post-pooling transformer also makes the compressor smaller and faster. Learned pooling followed by direct projection provides the strongest quality and efficiency among the tested compressor designs.

We show that training a single d-dimensional steering vector per layer with reinforcement learning, while freezing all base weights, matches the accuracy of fully RL-tuned reasoning models on mathematical-reasoning tasks.On an 8 billion-parameter model this adds only ≈ 0.0016% additional parameters and reproduces performance across a range of base models and mathematical-reasoning benchmarks.These results tighten the upper bound on the parameter budget required for high-level chain-of-thought reasoning, indicating that millions of adapter weights are unnecessary.The minimal trainable footprint reduces optimizer memory and inter-GPU communication, lowering the overall cost of fine-tuning.Moreover, a logit-lens analysis shows that the learned vectors amplify coherent token directions, providing clearer insight into the model’s internal computations.