GRPO is a reinforcement learning algorithm that fine-tunes language models by normalizing rewards across sampled rollouts, avoiding a separate value network.
Epoch AI's InnovationEval credited GPT-5.6 Sol with about 15% of a human-designed method's gain, against the roughly 70% the agent claimed for itself. Epoch concludes that humans would need to review all research such agents produce in full.
Reality
NVIDIA's LoGRA cuts average RL post-training memory by up to 45.7% by storing gradients as low-rank sketches, according to a write-up of its paper. With that saving, a 27B model trained stably for over 1,100 steps on one eight-GPU node where dense Adam runs out of memory.
Reality
AWS has generalised the orchestration for multi-turn reinforcement fine-tuning. The part that decides what the model learns is still code somebody on your team has to write and audit.
Reality
PewDiePie says OpenAI banned him twice while he used GPT-5.6 Sol outputs to train Ajax, a 9-billion-parameter model for home PCs. OpenAI has not commented publicly, but his account shows that a team training its own model on a frontier lab's answers can lose API access partway through the build.
Perspective Coverage
4 publishersReality
Cantina released Apex Flash-1, an open-weights security model fine-tuned on 50 of its own vulnerability cases. Its only performance evidence is a 60-task evaluation the company ran itself, and a separate build is modified to refuse fewer requests.
Reality
Qwen2.5-3B, wired to a local Wikipedia index, scored 52% on 150 post-cutoff questions it answers none of unaided, up from 33%, in a dev.to author's tests. Each fix targets a measured 3B failure, so a zero-shot 7B gained only 9 points from them, and the two readers' confidence intervals overlap.
Reality
UW and Meta researchers report 59.4% on BrowseComp-Plus for a model that edits its own context, against 53.4% for Codex-style summarisation. The edited file is thrown away when the task ends, so memory that lasts across tasks is still the builder's job.
Reality
Authors of a dev.to trainer comparison say summing task, cost and guardrail rewards can leave up to a third of GPU batches with zero gradients. They back per-channel normalization, as in GDPO, and want guardrails enforced as hard rules.
Reality
AWS says running DeepEP over its EFA network on EKS gives mixture-of-experts reinforcement learning 40% more throughput. Whether that reaches another cluster depends on how much of each training step goes to expert traffic between nodes.
Reality
One 8,192-token session on a 27B model holds 512 MB of key-value cache, or 64 KB for every token generated. How many of those sessions fit in free VRAM sets serving concurrency, and paging decides the waste.
Reality
A dev.to walkthrough of DeepSeek's GRPO puts a 70B PPO training loop over 600GB of VRAM before any sharding. The critic-free version deletes the most expensive network and pays for it in sampled completions.
Reality
An arXiv paper names the failure behavioral state decay and runs a side-car memory agent next to an unmodified action agent, reporting gains of 8.3 and 6.8 points of pass@1 on two long-horizon benchmarks.
Reality
Retrieve-for-Train runs reinforcement learning once against a fixed corpus and distils the winning sub-query sets into a lightweight diffusion model. Adopting it puts a retrain schedule on whoever owns the catalogue.
Reality
The band it joined is one where the best closed models still finish under 10% of tasks end to end at roughly $50 and 20 minutes each, so what post-training buys a law firm is price and hosting rather than capability.
Reality
Greedy accuracy came back to baseline by 1,500 steps and per-sample correctness tripled, so the fall in pass@64 to 0.19 only surfaced when the authors paid for 64 samples a problem instead of one.
Reality
Three LoRA stages on Qwen2.5-0.5B pushed reward to 1.0 and accuracy below the untrained base. The fix that recovered 43 points was an execution harness that runs both queries and compares the rows.
Reality
Thinking Machines' own figures show that better prompts carried the models it tested from a coin flip to the mid-70s and then quit, leaving the last five points to an 80% trust bar to be bought with annotation time from the analysts being freed.
Reality
A year of SFT and GRPO on 9B to 35B vision-language models, and the costliest bug never threw an exception. It supervised prose and scored a single letter.
Reality
The model now writes its own training tasks and grading harnesses. That removes the bottleneck of hand-built tasks and replaces it with a harder one: rewards that cannot be gamed.
Reality
An open-source stack pairs a deterministic Minecraft reimplementation with seed-level provenance, so a reinforcement learning result can be replayed instead of reconstructed.
Reality