Let's (not) just put things in Context: Test-time Training for Long-context LLMs

Progress on training and architecture strategies have enabled LLMs with millions of tokens in context length. However, empirical evidence suggests that such long-context LLMs can consume far more text than they can reliably use. On the other hand, it has been shown that inference-time compute can be used to scale performance of LLMs, often by generating thinking tokens, on challenging tasks involving multi-step reasoning. Through controlled experiments on sandbox long-context tasks, we find that such inference-time strategies show rapid diminishing returns, and fail at long context. We attribute these failures to score dilution, a phenomenon inherent to static self-attention. Further, we show that current inference-time strategies cannot retrieve relevant long-context signals under certain conditions. We propose query-only test-time-training (qTTT) that, through targeted gradients updates on the given context, provably overcomes limitations of static self-attention. We find that this simple shift in how inference-time compute is spent leads to consistently large performance improvements across models and long-context benchmarks. qTTT leads to massive 12.6% and 14.1% points improvements for Qwen3-4B on average across subsets of LongBench-v2 and ZeroScrolls benchmarks. The takeaway is practical: for long context, a small amount of context-specific training is a better use of inference compute than current inference-time scaling strategies like producing more thinking tokens.