Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance
Researchers introduce FEST, a technique that combines reinforcement learning with minimal supervised demonstrations to improve sample efficiency in language model training. The method achieves strong results using only 128 randomly selected examples, addressing a critical bottleneck where RL struggles on hard reasoning tasks like math and coding. This work matters because it reduces the annotation burden that typically makes demonstration-guided RL prohibitively expensive, potentially lowering the cost barrier for developing capable reasoning models across organizations with limited labeling budgets.
Modelwire context
ExplainerThe paper's core claim rests on a specific constraint: that RL reward signals themselves can be bootstrapped from randomly selected examples rather than requiring dense annotation of every trajectory. This inverts the typical bottleneck from 'we need labeled data' to 'we need labeled reward signals,' which is a narrower problem.
This connects directly to the CAST framework from the same day, which also embeds learned insights into reward signals during RL to handle complexity decisions in tool use. Both papers treat reward design as the actual leverage point rather than data volume. FEST tackles reasoning tasks where rewards are sparse; CAST tackles tool-use reliability where rewards must be context-sensitive. Together they suggest a pattern: RL for LLMs is becoming less about 'more data' and more about 'better reward signal design.' The DiffusionOPD work on multi-task RL distillation also shares this focus on reward architecture rather than data scaling.
If FEST's 128-example gains replicate on held-out math benchmarks (AIME, Putnam) that weren't in the original training set, that confirms the method generalizes. If results degrade when the 128 examples are adversarially selected rather than random, that signals the approach is actually relying on coverage rather than efficiency.
Coverage we drew on
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MentionsFEST · RLVR · LLMs · chain-of-thought
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