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ShieldVLA is proposed, a safety-aligned fine-tuning framework for VLA models based on Hamilton-Jacobi (HJ) reachability that reduces cumulative safety cost by 57% on average and improves task success rate by +0.13 over SafeVLA.
SNIPER is presented, a two-stage structured pruning framework that solves a knapsack optimization over coarse-granularity components to yield conditionally optimal parameter allocations with respect to fixed importance estimates, followed by a fine-grained pruning stage to meet strict budget constraints.
This work presents Echoverse, which compiles specifications into stateful applications whose tasks are graded against the application's own database, and a co-evolution loop that reads every graded rollout twice: as repairs to the environment, its tasks and its verifier, and as training signal for the model.
This work reproduces SDPO's reported gains in its easy setting, then applies the identical setup to difficult tasks and finds that it does not teach anything, and explains this failure through a single causal chain from the loss to the model it produces.
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