No Free Lunch: The Hidden Costs of Using Internal Feedback for LLM Reasoning
Large language models (LLMs) have become increasingly sophisticated, thanks in part to reinforcement learning techniques like Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR). But what if we could skip the external supervision altogether? A new study explores Reinforcement Learning from Internal Feedback (RLIF), a method that relies solely on the model's own signals—like token-level entropy and self-certainty—to improve reasoning. The results? A mixed bag.
The Promise of RLIF
RLIF methods leverage intrinsic metrics to guide model training without external rewards. The study examines three key internal signals:
- Self-certainty: Measures the model's confidence in its predictions.
- Token-level entropy: Captures uncertainty at each token generation step.
- Trajectory-level entropy: Evaluates the overall uncertainty of the generated sequence.
Theoretical analysis shows these signals are partially equivalent, all driving the model toward lower policy entropy—essentially making the model more "confident" in its outputs.
The Catch: Diminishing Returns
While RLIF initially boosts performance for base models (like Qwen2.5-3B and Qwen3-1.7B), the gains are short-lived. After about 20 training steps, performance peaks and then declines, sometimes falling below pre-training levels. Even worse, RLIF offers little to no improvement for instruction-tuned models, which already operate with lower entropy.
The study attributes this to a trade-off between underconfidence and overconfidence. Early in training, RLIF helps models avoid excessive hesitation (underconfidence), but as training progresses, it pushes them toward shallow, premature conclusions (overconfidence). This is reflected in the declining use of "transitional words"—phrases like "Wait, let me check"—that signal deeper reasoning.
Practical Implications
The findings suggest RLIF is best suited for base models with high initial entropy. For instruction-tuned models, the benefits are negligible or even harmful. The study also highlights the importance of monitoring transitional words as a proxy for reasoning depth.
The Bottom Line
RLIF isn't a free lunch. While it offers a cost-effective alternative to externally supervised methods, its effectiveness depends heavily on the model's starting point. For now, the best approach might be a hybrid: using RLIF to kickstart base models before switching to more traditional fine-tuning methods.