Post-Training Overtakes Pre-Training as the Locus of Model Improvement

The competitive frontier in large language models is migrating from pre-training scale to post-training technique, and October's vendor disclosures make the shift measurable for the first time. Third-party statistics cited by a Chinese-language industry aggregator project that the share of compute devoted to pre-training will fall from 67% in Q1 2024 to 7% in Q4 2026, while post-training plus reinforcement learning rises to 55%². If those figures hold even directionally, the economics of foundation-model development are inverting: the expensive, capital-intensive phase that defined the scaling-laws era is becoming a minority line item, and the iterative work done after a base model exists is becoming the main event.

The thesis is not speculative. Two Chinese labs demonstrated it in consecutive weeks. On October 17, DeepSeek updated its V4.1 Flash to the V4.1-Flash-1017 snapshot without changing the architecture, tokenizer, or parameter scale; all improvements came from post-training. Seven days later, on October 24, Moonshot AI released Kimi K3.1 Pro, which the company positioned as a post-training version of K3, retaining 2.8 trillion total parameters, 104 billion activated parameters, a one-million-token context window, and native image and video input capabilities. Both vendors' messaging was consistent: same base, better model.

The benchmark gains are not marginal. DeepSeek's vendor-reported results show all 11 benchmarks rose in the 1017 snapshot, with gains ranging from 2.0 to 11.1 points. Terminal-Bench 4.0 jumped from 31.2 to 42.3; HLE rose from 36.8 to 42.6; DeepSWE v1.1 went from 74.2 to 77.0; ProgramBench climbed from 20.3 to 29.1. Kimi K3.1 Pro scored 65.7% on Terminal-Bench 4.0 in vendor self-reported results, compared with 13% for K3 in a third-party retest. Moonshot AI attributed the improvement to substantially increased post-training compute. The K3-to-K3.1 Pro jump on Terminal-Bench 4.0, from 13% to 65.7%, is the most dramatic single-benchmark gain in the packet, and it arrived with no disclosed change to the base model.

What makes these results commercially significant is the cost curve. On DeepSWE, the 1017 snapshot achieved 77.0% at an average of $0.2 per task. GPT-6 Astra, Gemini 3.8 Flash, and Claude Opus 5 fall in the $2 to $10 per-task range on the same benchmark. DeepSeek says API prices remained unchanged for the 1017 snapshot. The combination of rising scores and flat-to-falling prices is the signature of a technique that extracts more capability from existing parameters rather than adding new ones, and it explains why call volumes responded: DeepSeek V4.1-Flash-1017's call volume was reported up 801% week over week.

DeepSeek disclosed that its V4.1 technical report focused on data, including task synthesis pipelines and environment construction pipelines, and that it introduced no new post-training algorithms, using the standard three-stage sequence of supervised fine-tuning, reinforcement learning, and on-policy distillation. CoreWeave, which operates GPU cloud infrastructure, framed the same dynamic from the supply side. Its October 5 blog post argued that production AI requires closing the loop between inference and post-training: production traces become training data, post-training needs compute and orchestration, and the resulting model returns to serving¹. CoreWeave announced RL Rollouts, CoreWeave Model Distillation, and a programmable training API as features connecting post-training back to inference.

ANALYSIS CoreWeave's productization of the inference-to-post-training loop is a bet that this workflow becomes recurring infrastructure demand, not a one-time research project. That aligns with the compute-share projections: if post-training plus RL absorbs 55% of frontier compute, the infrastructure providers who make that loop frictionless capture a growing share of spend.

The open-weights community is running a parallel experiment at smaller scale. A developer posted on r/LocalLLaMA that Sabi-Noema, a post-trained version of Qwen 3.8 27B, beats GPT-6 Luna-medium on DeepSWE and beats Luna-high on agentic and tool-calling benchmarks like TB2.1, Tau-3 bench, ClawEval, and BFCL v4⁴. The same post reported that Sabi-Noema generates 50% fewer tokens per response on TB2.1 and 25% fewer tokens on DeepSWE compared to base Qwen, and that an MLA conversion reduced KV cache size to 8.4x smaller with similar performance. Separately, another r/LocalLLaMA user ran a 469-problem domain-specific evaluation comparing Qwen 3.8 27B fine-tunes against frontier models, finding that Opus 5.5 topped the list at 99.6% accuracy but that fine-tuned 27B models could close the accuracy gap at the cost of latency⁵. These community results, while not independently verified, reinforce the same pattern visible at the vendor level: post-training on a fixed base yields disproportionate capability gains.

The research pipeline points in the same direction. Thinking Machines Lab, which raised $1 billion in September, published an experiment showing on-policy distillation can match or exceed reinforcement learning on math tasks using roughly one-fiftieth to one-hundredth of the compute. If distillation can substitute for RL at that ratio, the cost of each post-training iteration drops further, accelerating the loop CoreWeave is trying to productize.

The cost structure of the old regime is worth recalling. DeepSeek-R1's pre-training, which relied on the V3 base, cost $5.57 million; its post-training cost was about $2 million. Luo Fuli, cited in the same aggregator report, said the ratio of research, formal pre-training, and formal post-training in the chatbot era was roughly 3:5:1, that top teams are now achieving a 1:1 ratio, and that the reasonable ratio in the agent stage should be 3:1:1. A 3:1:1 ratio for the agent era would mean post-training and pre-training each consume equal formal compute, with research tripling both. That is a structural inversion from the 3:5:1 chatbot-era baseline.

Independently verified benchmark data from our terminal confirm that the base models being post-trained are themselves strong. Qwen 3.8 27B scores 87.50 on ARC-AGI-1 and 42.36 on ARC-AGI-2. DeepSeek-V4.1-Flash's arena text factuality Elo is 1468.99, up 35.5 points from V4-Flash. Kimi K3's SimpleQA verified score is 50.60, up 15.7 from K2.6. These independently verified gains across model families reinforce the vendor-reported post-training results, though the largest benchmark jumps (Terminal-Bench 4.0 for K3.1 Pro) remain vendor-claimed and await independent confirmation.

Pricing tells its own story. As of October 31, Qwen 3.8 27B lists at $0.50/$3.00 per million tokens in and out. GPT-6 Luna lists at $0.10/$0.125. Claude Opus 5.5 lists at $4.00/$20.00. Opus 5.5's input price is 8x Qwen 3.8 27B's ($4.00 versus $0.50); its output price stands at $20.00 against Qwen's $3.00. For high-volume API consumers, the post-training cost advantage of cheaper base models that close the quality gap through iterative refinement is commercially decisive.

What to watch: whether Kimi K3.1 Pro's 65.7% Terminal-Bench 4.0 score holds under independent retest, given that K3's third-party-retested score was 13% against a higher vendor claim. Whether the 7% pre-training compute share projected for Q4 2026 is corroborated by any lab's public disclosure. Whether Thinking Machines Lab's distillation-versus-RL compute ratio replicates beyond math tasks. And whether Moonshot AI, which IPO'd at a $5 billion valuation in September, discloses post-training compute budgets that let outsiders verify the cost economics it claims.