P2T derives token rewards from an existing process reward model
A gradient estimator attributes a sequence-level reward to individual tokens without training another reward model.
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A gradient estimator attributes a sequence-level reward to individual tokens without training another reward model.
PRISM combines numerical features, ECG history and population representations, while its reported comparisons expose different input conditions.
A research framework diagnoses failed trajectories and gives targeted instruction during verifiable-reward training.
A research prototype learns constraints from accepted and rejected solutions, then generalizes them across problem sizes.
A theoretical study asks what can be identified when several causal graphs remain compatible with the available information.
A benchmark separates task planning from robot control and tests failures, interruptions and coordination over longer missions.
Selective branches reuse a shared prefix so a student model can receive teacher feedback on more possible paths.
Two agents share a workspace while session limits and context resets help test a workflow for extended engineering tasks.
Step-wise Truncation Probing compares answers from prefixes of the same reasoning trajectory to characterize overthinking.
Cross-teacher feedback and gradient controls aim to reduce interference when a model learns several domains at once.
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