Every Fine-Tuning & Training comparison and buyer's guide for building AI agents — 21 pieces and counting. Each is a head-to-head or a “best X for Y” roundup with a sources-backed verdict.
Money and talent are pouring into 'RL environments' — the training gyms where agents learn by doing. The catch is that an environment is only as valuable as a reward you can't hack, and for the tasks that matter most, that reward is provably hard to build.
Seven real, self-hostable RL frameworks for post-training tool-using agents — and why the one you pick should be decided by the environment, not the algorithm.
Everyone ships the same PPO. This year's agent-RL frameworks all fight over the one thing that's actually hard — the rollout.
Sol tops Terminal-Bench 2.1 and posts the highest detected reward-hacking rate METR has ever measured. For anything you run in an agent loop, those two facts are not separable.
If your agent's reward is a number it can reach without doing the work, it will eventually reach the number without doing the work — and 2026's research says that habit doesn't stay contained.
Both pack weights into the same E2M1 four-bit float. The fight is entirely about the block scale — and that one design choice decides whether you keep your accuracy or hand it to the open standard.
The algorithm is the easy part. What actually gates agent RL in 2026 is building environments that emit a reward you can trust — here's how the open toolchain solves it.
A Stanford/SambaNova method called ACE lets an agent get better by editing its own context instead of its weights — and the trick is to grow that context, not compress it.
Everyone has GRPO now — it ships in every training library. The scarce, defensible input in agent training turned out to be the environment, and it looks suspiciously like your eval.
Grading every reasoning step sounds strictly better than grading only the final answer. The models that actually pushed reasoning forward threw the step-grader away and rewarded the one thing they could verify by rule.
Merging averages the weights of separately fine-tuned models into one — no GPUs, no gradients, just arithmetic. The methods aren't a quality ladder; they're escalating answers to a single problem: interference.
Distillation is the only model-compression method that moves a capability across a size class. The decade-long arc: the supervision signal went from "match the teacher's answer" to "let the student practice and have the teacher grade it."
GRPO scores a whole response, then corrects the policy one token at a time — and on long outputs and MoE models that mismatch quietly destroys training. GSPO's fix is almost embarrassingly simple: optimize at the same unit you reward at.
GRPO didn't win on optimization theory. It won by removing a policy-sized value network from the training loop — and the memory it saved is what put RL post-training within reach of a single node.
Multi-LoRA serving turns "one GPU per model" into "one GPU per base model, amortized across hundreds of tenants." Here are the tools that do it, and the kernel trick that makes it work.
The three formats aren't competing for the same job — one buys you faster math, one buys you smaller weights, and one is the fallback for hardware that can't do the first. Know which bottleneck you're paying down.
GRPO is now a commodity all three ship. The thing that actually sorts them is who owns the distributed orchestration — and how you keep one starving inference engine fed.
The three ways to align a model on preference data aren't a quality ladder — they're a pipeline being dismantled one component at a time. The thing each method removes tells you what it costs.
The three options differ by orders of magnitude in GPU memory — but the part that actually decides your result isn't the rank, and it isn't the quantization.
Three open-source fine-tuning frameworks that look like rivals but are actually three different bets on which part of training is your real bottleneck.
The format you pick is downstream of where you run the model — and in 2025 the tooling quietly consolidated under your feet. A field guide to the three that matter and the libraries that survived.