#credit-assignment
3 articles
01
·Distillation & Compression·★ MEMBER·PAPER·10 min read
Distilling Agents — How to Compress a Long Trajectory
When you distill an agent, the unit of learning changes from a single question-and-answer to an entire game. Tens of thousands of tokens come back with one bit of grading. This article works through how that gap is bridged — turn-level credit assignment, trajectory filtering and on-policy correction, and the inheritance of tool use — and maps the recent long-horizon agent literature onto those three axes.
02
·Training & Alignment·★ MEMBER·PAPER·11 min read
Paper Deep-Dive: ABSeeker — Training Long-Horizon Search Agents by Grading Each Step Backward from the Answer
When an agent chains dozens of web searches, the only reward is whether the final answer was right — so every good move and every bad move along the way gets graded identically. We walk through ABC (Answer-Backtracked Credit Assignment), which recovers clues by working backward from the answer and scores every step against them, and ABSeeker, the 4B model trained with it — all from the paper itself.
03
·Inference & Serving·★ MEMBER·PAPER·9 min read
Paper Deep-Dive: AgentOPSD — Finding the Turn That Won the Game with Recursive Bayesian Belief Updates
When a long multi-turn episode only tells you 'success' or 'failure' at the very end, which turn actually mattered? AgentOPSD reinterprets self-distillation gaps as Bayesian evidence and recursively updates a success belief in log-odds space. We walk through the mechanism, the 89.1% ALFWorld result, the ablations, and the limitations — all from the paper itself.