Quantify Trait Amplification vs New Learning in RLMT Gains

Determine the relative contributions of (i) amplification of pre-existing traits in the base language model and (ii) acquisition of new traits during the supervised fine-tuning warm-start and during Reinforcement Learning with Model-rewarded Thinking (RLMT) to the observed performance improvements in RLMT-trained language models. Ascertain how much each stage—SFT warm-start and RLMT—contributes to these gains to inform the design of improved post-training pipelines.

Background

The paper introduces Reinforcement Learning with Model-rewarded Thinking (RLMT), which combines long chain-of-thought generation (as in RLVR) with online reinforcement learning against a reward model (as in RLHF) to improve general-purpose chat and open-ended tasks. Across multiple backbones (Llama-3.1-8B and Qwen-2.5-7B) and algorithms (DPO, PPO, GRPO), RLMT consistently outperforms standard RLHF on chat and creative writing benchmarks.

Despite these gains, the authors highlight uncertainty about the mechanism behind the improvements: whether RLMT primarily amplifies reasoning and planning traits already present in the pretrained models, or whether it induces learning of new capabilities during the SFT warm-start and/or the RL training stages. Clarifying this decomposition would guide the design of more effective post-training pipelines and the allocation of effort across SFT versus RL.

References

While our work finds the effectiveness of training LMs with thinking, it is unclear how much of the improvement is due to amplification of traits already present in the model, versus the learning of new traits during the SFT warm-start or RL training.

— Language Models that Think, Chat Better  (2509.20357 - Bhaskar et al., 24 Sep 2025) in Limitations and future work, Conclusion section

The amplification result holds for those framings, and we have not tested whether it holds under others.

— Training Natural Schemers: RL Can Amplify Oversight-Conditional Behavior  (2610.26551 - Jaworski et al., 19 Oct 2026) in Appendix, Section 'Limitations of the RL amplification result', paragraph 'Each environment runs at its own framing'

First, ablating the traits themselves: removing one SDF trait at a time (helpfulness maximization, rules as instruments, calibration), dropping or varying the oversight-knowledge block, and reweighting the violation penalty would establish which ingredients are necessary rather than sufficient.

— Training Natural Schemers: RL Can Amplify Oversight-Conditional Behavior  (2610.26551 - Jaworski et al., 19 Oct 2026) in Appendix, Section 'Extended future work'