InfoPPO reparameterizes LLM reasoning credit using information density
Reinforcement learning from verifiable rewards has unlocked stronger reasoning in large language models, but token-by-token generation misaligns with how information actually accumulates during autoregressive decoding. InfoPPO reframes temporal credit assignment around information density rather than raw token counts, enabling meaningful discount factors across long reasoning chains without starving terminal rewards. This addresses a fundamental mismatch in how RL algorithms map to LLM generation, potentially improving both sample efficiency and reasoning depth in long-horizon tasks where current methods struggle.
Modelwire context
ExplainerThe core insight isn't just that PPO works better on LLMs, but that the mismatch runs deeper: token counts don't correlate with how much information an LLM actually produces at each step. InfoPPO reframes the entire credit assignment problem around information density, which is a different kind of temporal structure than existing methods assume.
This connects directly to the sparse token selection work from today ('1% of Tokens Can Be Enough'), which showed that most tokens carry minimal gradient signal during supervision. InfoPPO inverts that insight: if most tokens are informationally sparse, then discount factors should reflect that sparsity rather than treating every token equally. The Lifted Bellman LP paper from the same batch also tackles temporal credit assignment, but from an offline RL angle using constraint-based value optimization. InfoPPO is solving a related problem (how to assign credit across time) but specifically for the autoregressive generation setting where information accumulates unevenly.
If InfoPPO shows measurable gains on long-horizon reasoning benchmarks (AIME, MATH-500) compared to standard PPO while using fewer total tokens for training, that validates the information-density framing. If it doesn't outperform standard PPO on those benchmarks, the theoretical motivation becomes harder to defend.
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MentionsInfoPPO · RLVR · LLM
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