Can LLMs Self‑Correct Their Answers? Exploring Reward Models, Loss Functions, and Training Dynamics

Answer Self‑Correction

One proposed engineering approach is to let a language model generate an answer while simultaneously evaluating it with an attached reward model. If the reward model flags the partial answer as poor, the system could either (1) clear the KV cache and restart generation, suppressing the previous output path, or (2) emit a special token such as <response_error> that signals a regeneration step. This idea parallels self‑refinement techniques like SPIN or self‑reward, but concrete experiments are still lacking.

Simulating Human Brain States

Interpretability research suggests that attention layers drive in‑context learning while MLPs store factual knowledge. A speculative question is whether hidden‑state dynamics exhibit chaotic patterns when the model “struggles” to answer, analogous to human discomfort. However, layer‑norm and the near‑one‑hot softmax distribution may limit observable chaos.

Model Awareness of Physical Parameters

Current LLMs do not have explicit access to their own architectural parameters (e.g., hidden size, maximum sequence length). This limits their ability to enforce strict token‑count constraints. Adding absolute positional embeddings to every attention layer—beyond the usual RoPE (rotary) embeddings—could improve the model’s sense of absolute position and potentially help with length‑aware generation.

Loss Function Design

Most recent work uses token‑level loss for supervised fine‑tuning (SFT) and sentence‑level loss for DPO/PPO. Potential extensions include:

Assigning different coefficients to high‑frequency versus low‑frequency n‑grams during pre‑training to balance learning dynamics.

Investigating the effect of penalizing special tokens such as <eos> in DPO, as these tokens can dominate loss gradients.

Exploring whether next‑token prediction should explicitly model negation (e.g., “Taiwan is not China”) and how politically charged statements influence conditional probability estimates.

Continual Pre‑Training Dynamics

Open questions about knowledge acquisition and forgetting during continual pre‑training include:

Why early training phases appear to acquire knowledge while later phases overwrite it.

How many repetitions of a fact are needed for stable retention.

What is the forgetting rate for rarely revisited facts.

Why training on domain B can degrade performance on domain C more than on domain A.

Whether mixed‑domain data consistently outperforms sequential domain training.

The Domain‑specific Continual Pre‑Training Scaling Law (D‑CPT Law) empirically describes these phenomena but does not explain the underlying mechanisms, leaving practitioners to rely on intuition and loss monitoring.

Model Architecture Trends

Recent architectural discussions highlight:

Flash‑Attention‑3 : Improves attention efficiency but does not eliminate the computational cost of RoPE and SwiGLU operators.

RNN‑style models such as RWKV and Mamba offer constant‑cost inference and compression of long‑range information, yet they remain less prominent compared to Transformers.

Hybrid approaches that compress distant context while retaining attention for nearby tokens are being explored but lack mature implementations.

PPO vs. DPO

Both Qwen2 and Llama 3.1 adopt Direct Preference Optimization (DPO) instead of Proximal Policy Optimization (PPO) because DPO provides more stable training and better generalization. Open research questions include:

Whether combining a reward model with PPO, DPO, or pure SFT yields higher final performance.

If the online nature of PPO offers theoretical advantages over the offline‑style DPO.

Reference

D‑CPT Law: Domain‑specific Continual Pre‑Training Scaling Law for Large Language Models – https://arxiv.org/abs/2406.01375