arXiv AI
When Does Feedback Help? Auditing Learned Voltage-Clamp Experimental Design
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Sep 12
When Validation Stops Learning: Auditing Update Admission for Continual Embodied Agents
The paper introduces an admission‑audit protocol for continual embodied agents, arguing that update admission should balance error control with retained learning opportunities within a fixed interaction budget. It critiques a range‑based confidence gate for failing to certify unchanged old‑task behavior, and proposes a paired‑binomial construction that reduces this burden when outcome disagreements are rare. Experiments on a one‑step pushing diagnostic show that fresh paired checks admit a significant portion of updates while the range‑based gate admits none, and a learned‑dynamics stress test helps distinguish model bias from feedback‑selection error.
arXiv Machine Learning
Jun 30
MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability
arXiv:2605. 26343v2 Announce Type: replace Abstract: Mechanistic interpretability seeks to explain a model's behaviour by finding its circuit: the sparse subgraph of the model's computation that is causally responsible for it.
arXiv Machine Learning
Oct 12
Cross-Behavior Policy Optimization: Unified Learning from Offline Demonstrations and On- and Off-Policy Rollouts via a Shared Regression Target
The paper introduces Cross-Behavior Policy Optimization (CBPO), a method that unifies offline demonstrations and policy-generated rollouts into a single regression loss using a fixed-reference target. CBPO eliminates the need for separate supervised fine‑tuning and reinforcement learning objectives, allowing positive and negative offline completions, reference responses, and current and historical rollouts to share a common reward‑labeled format and replay buffer. Experiments on two model scales and four mathematical reasoning benchmarks show that CBPO, especially the CBPO‑Unified variant, outperforms baseline methods in avg@32 and pass@32 metrics, with historical rollouts providing additional gains.
arXiv AI
Sep 18
Don't Mask the Environment: Observation Supervision Changes How Agents Explore Under RL
The paper introduces ActObs, a supervised fine‑tuning method that, unlike standard approaches, also predicts environment observations in agent trajectories. While both ActObs and action‑only training perform similarly after initial fine‑tuning, ActObs diverges during subsequent reinforcement learning, yielding higher pass@k scores on several benchmarks and better cross‑domain task performance. The authors attribute this advantage to ActObs’s joint supervision, which preserves observation gradients and prevents the policy from over‑specializing on actions alone.
arXiv Machine Learning
21h ago
An Explicit Feedback Contract for Iterative ML-Agent Harnesses
arXiv:2610.28798v1 Announce Type: new Abstract: Iterative ML agents develop models by editing code, running experiments, reading a validation score, and choosing what to try next. In current harnesse...
arXiv Computer Vision
Oct 2
DexPolicy: Scheduled Exploration for Trajectory-Guided Dexterous Manipulation
arXiv:2610.00360v1 Announce Type: cross Abstract: Reinforcement learning (RL) for dexterous manipulation must discover finger-object contacts and then control the object precisely; the action noise t...