Thomas Wolf

We’ve been hard at work trying to make models work with harnesses better. The best thing @yb2698 found: “harness adapters”. Small LoRAs trained with RL inside the actual agent harness they’ll be used with. Same base model. Different adapter for pi, opencode, hermes, etc. Why? Simply because the same model behaves very differently depending on the tools, prompts and control flow around it. Instead of asking one set of weights to be optimal everywhere, we can specialize a tiny part of the model for each environment. Early results are pretty encouraging: on held-out tasks, the adapters make fewer tool calls than the base model while keeping or improving accuracy. And because they’re LoRAs, the operational side is surprisingly cheap too. For one LFM2.5-24B-A2B experiment, we evaluated 9 checkpoints / 1,350 agent sessions on a single H200 in under an hour. The total: ~$5! Merging and serving every checkpoint separately would have cost around $90. There’s another important piece: RL agents will eventually try to hack the reward. So every tool call goes through `safeagents`, a deterministic rule-based guard that blocks things like editing tests, tampering with rewards or bypassing the sandbox before they execute. And the whole loop can start from real usage: your coding sessions -> verifiable environments -> RL -> a better adapter for your agent. The model doesn’t have to change completely. Sometimes it just needs to get better at the harness it actually lives in. huggingface.co/blog/harness-…