Aligning Language Models Using Multi-Objective Deep Reinforcement Learning
Bibliographic record
Abstract
The alignment techniques used in state-of-the-art language models (LMs), e.g., reinforcement learning from human feedback (RLHF), have driven many successful natural language processing (NLP) tasks. RLHF uses human preferences based on the guidelines of being helpful and safe as a single reward signal to fine-tune language models. However, the trade-offs between helpfulness and safety are often found to be a problem, which makes it difficult for a model trained towards one objective to perform well on both. This paper proposes a new alignment technique, multi-objective language model alignment (MOLMA). The framework is based on multi-objective deep reinforcement learning to fine-tune language models. MOLMA can efficiently address the conflicting or the dominating learning signal issue caused by the trade-offs of inherent, often conflicting, multi-objectives underlying the language model alignment task. From the overall objective of achieving helpfulness and safety, our results show that MOLMA outperforms the other alignment techniques that rely on single-objective deep reinforcement learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".