Pushing LLMs to 1M token context has, over the past two years, repeatedly come back to the same dead end: full attention is too compute-heavy, sparse attention can't pick chunks well. The HiLS (Hierarchical Landmark Sparse) Attention thrown out by arXiv 2607.02980 gives a third path — putting "chunk selection" into end-to-end training with the LM loss, instead of making do with mean-pooling or heuristic rules. HiLS explicitly writes the retrieval score into forward attention: query interacts with chunk landmarks for scoring, then the output of each retrieved chunk is fused by this score, and gradients flow directly back to the retrieval head. This is equivalent to jointly optimizing "knows how to select chunks" and "knows how to use chunks" with the same objective function, mechanically solving the common problem of predecessors like NSA, DashAttention, and InfLLM v2: "has retrieval but not accurate enough". The results are quite hard-core: on a 345M model with 8K training context, RULER 512K single-needle retrieval still maintains 99% accuracy, and at 1M token it can still hit 96% — 64× length extrapolation; after switching the Olmo3-7B base to HiLS, it beats Full-Attn HoPE with no more than 2K tokens activated; training a 1.4B from scratch for 300B tokens, sparse training and dense are almost aligned on in-domain tasks. The real side product is that "infinite-context training" becomes feasible for the first time: training length is naturally limited by attention cost, but as long as chunk selection is accurate enough, the fixed overhead of sparse retrieval allows 256K, 1M, or even longer training context compute to stay bounded. HiLS uses end-to-end learning to pull "long context" from engineering tricks back to mathematical modeling — once sparse retrieval is accurate, how many token lengths to chain together afterward is just a compute budget problem.