… Together these results signal that the agent-alignment lever is shifting from classical model alignment and prompt steering toward harness alignment and skill steering.
… To this end, we shift the distillation target from endpoint consistency mapping z {t}rightarrow z {0} to flow-map transition learning z {t}rightarrow z {r} over arbitrary time intervals. …
… A core observation is that the gap between open and closed source is shifting away from raw image quality and toward data engineering, post-training, long context, multi-turn consistency, tool use, and verification closed loops. …
… Shifting evaluation from static factual recall to psychological trajectory alignment feels like the right direction for RPLAs, and the out-of-source-text scenarios are a clever stress test where retrieval genuinely can't help. …
… Prior work mitigates this by using LLMs to rephrase queries and corpora into a normalized style, but leaves two questions open: how much representational shift helps, and when is the per-query LLM call justified? …
… Unfortunately, optimizing such behavior presents a sharp dilemma in practice: online reinforcement learning is able to effectively address multi-turn dynamics but is prohibitively expensive due to the cost of generating full correction trajectories at every update, whereas offline supervised fine-t… …
… The resulting natural-language skills can be plugged into any language model at inference time to enhance context learning capability. ctx2skill is cool in spirit, but i keep coming back to the binary judge as a potential bottleneck that could push the loop into brittle, overfit skills. the cross-t… …
… The two compose: claim→evidence closes internal consistency, evidence→world closes the shared-error gap the trajectory can't see by construction. the claim ledger in DRIFT is a nice hinge between what agents say and what the evidence actually supports. my main question: how does DRIFT handle retroa… …
… A brilliant shift from syntactic variation to genuine semantic exploration! …