inject-golden-answer-off-policy-trajectory-during-rl TEC-0133 · proposedInject the golden answer as an off-policy trajectory during
During GRPO-style knowledge-injection RL, add the gold answer to each rollout group and apply it as a direct supervised gradient scaled by its group-relative advantage (no importance ratio or clipping), so learning signal survives when all
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
During GRPO-style knowledge-injection RL, add the gold answer to each rollout group and apply it as a direct supervised gradient scaled by its group-relative advantage (no importance ratio or clipping), so learning signal survives when all Filed by the drafting stage from From Memorization to Absorption: Mixed-Policy RL for Continual Knowledge Injection (2608.25243) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: On two new document benchmarks (Blank, 776 corpora; Counter, 252 counterfactual corpora) judged by an LLM, against SFT baselines (PIT, Self-Tuning, Autonomous Learning), vanilla GRPO, and LUFFY mixed-policy RL; GRIN reached 21.16% multi-source and 31.69% inferential on Blank vs Self-Tuning's 11.26% and 7.10%, similar single-fact recall, and the lowest prior-belief fail@k (24.05%); baselines trained to 50 epochs still plateaued below it.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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