On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
- URL: http://arxiv.org/abs/2605.08368v1
- Date: Fri, 08 May 2026 18:23:25 GMT
- Title: On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
- Abstract summary: We argue that post-training research should distinguish between capability elicitation and capability creation.<n>We develop this argument through a free-energy view of post-training.
- Score: 13.996919933596153
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Debates about large language model post-training often treat supervised fine-tuning (SFT) as imitation and reinforcement learning (RL) as discovery. But this distinction is too coarse. What matters is whether a training procedure increases the probability of behaviors the pretrained model could already produce, or whether it changes what the model can practically reach. We argue that post-training research should distinguish between capability elicitation and capability creation. We make this distinction operational by introducing the notion of accessible support: the set of behaviors that a model can practically produce under finite budgets. Post-training that reweights behaviors within this support is capability elicitation; whereas changing the support itself corresponds to capability creation. We develop this argument through a free-energy view of post-training. SFT and RL can both be seen as reweighting a pretrained reference distribution, only with different external signals. Demonstration signals define low-energy behavior for SFT, and reward signals define low-energy behavior for RL. When the update remains close to the base model, the main effect is local reweighting, not capability creation. Within this framework, the central question is no longer whether post-training is framed as SFT or RL, but whether it reweights behaviors already within reach, or instead expands the model's reachable behavioral space through search, interaction, tool use, or the incorporation of new information.
Related papers
- What Does an LLM Learn from Reinforcement Learning? A Mechanistic Interpretability Perspective with Fixed-SAE Track [4.624097215050827]
We introduce Fixed-SAE Track, a framework that trains one shared SAE per layer on activations pooled across the base model and all RL checkpoints.<n>We find that RL-induced drift is small, gradual, concept specific, and concentrated in late layers, mainly enhancing the sampling rates of a small set of ladder tokens.
arXiv Detail & Related papers (2026-09-14T05:29:45Z) - Demystifying Reinforcement Learning Post-Training of Language Models [32.79823433295599]
Reinforcement learning (RL) post-training has emerged as a powerful framework for enhancing the capabilities of large language models (LLMs)<n>Yet for many researchers and practitioners, the principles behind classical RL remain a "black box"
arXiv Detail & Related papers (2026-08-24T18:39:45Z) - Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models [51.45122910254346]
Reinforcement learning (RL)-based post-training often improves the reasoning performance of large language models beyond the training domain.<n>Supervised fine-tuning (SFT) frequently leads to general forgetting capabilities.<n>We present a feature-level mechanistic analysis methodology to probe RL generalization using a controlled experimental setup.
arXiv Detail & Related papers (2026-04-27T21:22:34Z) - SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models [51.99912169291891]
Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models.<n>We propose a training paradigm that interleaves conventional RL with inverse reinforcement learning (IRL)<n>Our findings suggest that alternating between RL and IRL offers an effective pathway toward extending the exploration capacity of reasoning-oriented large language models.
arXiv Detail & Related papers (2026-04-18T13:49:47Z) - On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models [73.10315509190623]
Recent reinforcement learning techniques have yielded impressive reasoning improvements in language models.<n>It remains unclear whether post-training truly extends a model's reasoning ability beyond what it acquires during pre-training.<n>We develop a fully controlled experimental framework that isolates the causal contributions of pre-training, mid-training, and RL-based post-training.
arXiv Detail & Related papers (2025-12-08T18:12:10Z) - Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions [17.407689582427437]
Large language model (LLM) reasoning has shown that sophisticated behaviors such as planning and self-reflection can emerge through reinforcement learning (RL)<n>We introduce a novel training approach, textbfReLIFT (textbfReinforcement textbfL textbfInterleaved with Online textbfFine-textbfTuning)<n>In ReLIFT, the model is primarily trained using RL, but when it encounters challenging questions, high-quality solutions are collected for fine-tuning, and the training process alternate
arXiv Detail & Related papers (2025-06-09T08:11:20Z) - Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning [93.00629872970364]
Reinforcement learning (RL) has become the dominant paradigm for improving the performance of language models on complex reasoning tasks.<n>We introduce SPARKLE, a fine-grained analytic framework to dissect the effects of RL across three key dimensions.<n>We study whether difficult problems -- those yielding no RL signals and mixed-quality reasoning traces -- can still be effectively used for training.
arXiv Detail & Related papers (2025-06-05T07:53:59Z) - Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining [74.83412846804977]
Reinforcement learning (RL)-based fine-tuning has become a crucial step in post-training language models.<n>We present a systematic end-to-end study of RL fine-tuning for mathematical reasoning by training models entirely from scratch.
arXiv Detail & Related papers (2025-04-10T17:15:53Z) - How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence [52.9442657690445]
Post-training is essential for the success of large language models (LLMs)<n>We compare base and post-trained LLMs from four perspectives to better understand post-training effects.
arXiv Detail & Related papers (2025-04-03T06:30:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.