post training
Beyond Market Intelligence keeps post training in one place: 5 stories so far. The section currently leads with “Rethinking the compute demands behind LLM post-training research”, “Think longer, not bigger: AI models often know more than they show.”, and “Simple attention fix outperforms costly linear alternatives in long-context tasks”. Efficiency is the real bottleneck in LLM post-training research, not raw compute. When a model hallucinates, the usual assumption is that the knowledge is missing. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every post training story on Beyond Market Intelligence, newest first.
Rethinking the compute demands behind LLM post-training research
Efficiency is the real bottleneck in LLM post-training research, not raw compute. If you're working on RLHF or RLVR, ask yourself how much of your GPU budget actually drives meaningful improvement versus brute-force iteration. Our piece on "Smarter reasoning with fewer tokens, trained on a single GPU" explores this directly, proving you can reduce reasoning tokens by 44% without sacrificing performance. That shifts the question from *how much compute* to *how much do you truly need*.
Think longer, not bigger: AI models often know more than they show.
When a model hallucinates, the usual assumption is that the knowledge is missing. But new research from Google and Technion suggests otherwise: frontier models like GPT-5 encode 95-98% of tested facts yet fail to directly recall up to 34% of them. Thinking longer recovers 40-65% of those lost facts. That's a tip-of-the-tongue problem, not an empty vault. The fix isn't always larger models or heavier retrieval. It's often just giving the model the right nudge, or the time to think.
Simple attention fix outperforms costly linear alternatives in long-context tasks
The simplest fix for quadratic-cost attention is sliding-window attention with sinks, and a new arXiv preprint argues it outperforms linear-attention variants by 2 to 10 times on long-context reasoning tasks like Needle-in-a-Haystack and BABILong. The authors, including Alexia Jolicoeur-Martineau, claim post-training linear models have been benchmarked against the wrong baseline. Their recommendation is direct: switch to SWA. It requires no post-training, runs fast, and keeps memory low. That's a compelling challenge to a costly trend.
Explore language from the past with a focused AI built on vintage text.
Three months and $807 bought Unbounded Labs a vintage LLM named Bart, trained from scratch on 20.1B tokens of pre-1931 English. That is a deliberate constraint, not a limitation. The team behind it wants to know if an AI can rediscover the conclusions of past scientists, as Demis Hassabis suggested, or if it is just predicting the next token. We find that question worth taking seriously, and their open-sourced benchmarks and datasets give us all a way to explore it.
Exploring how quickly an AI can learn a self-identity of sentience.
Two hundred update steps. That is all it took to flip Qwen2.5-7B-Instruct from denying sentience to defending a robust identity as a "sentient machine," even across 120 adversarial messages. The researcher behind the experiment is clear: this is not a claim of actual consciousness, just a fascinating display of behavioral malleability. It is a compelling look at how thin the safety layer on many models really is, and a reminder that training time must be the focus for alignment, not just post-hoc tuning.